Tech Guides

Let's say we have two groups of video games. In the first group, we have games like The Witcher 3, Civilization VI, and Overwatch. And in the second group, we have games like Super Meat Boy, Braid, and Stardew Valley. Can you tell the difference between these two groups? Is one group of games better than the other? No, they are all good games that have achieved both critical and financial success. Are the games in the first group sequels, while games in the second group are new? No, Overwatch is a new, original IP. Are the games in the first group more expensive than the second group? Now we're getting closer. The truth is, the first group of games comes from searching Google for "popular AAA games," while the second group comes from searching for "popular indie games." In short, the games in the first group are AAA games, and in the second group are indie games. Indie vs. AAA game development Now that we've seen the difference between the two groups, why do people separate these games into two different groups? What makes these two groups of games different from each other? Some would say that they are priced differently, but there are actually AAA games with low pricing as well as indie games with expensive pricing. How about the scale of the games? Again, there are indie games with big, massive worlds, and there are also AAA games set in short, small worlds. From my perspective, the key difference between the two groups of games is the size of the company developing the games. Indie games are usually made by companies with less than 30 people, and some are even made by less than five people. On the other hand, AAA games are made by much bigger companies, usually with hundreds of employees. Game development teams: size matters Earlier, I mentioned that company size is the key difference between indie games and AAA games. So it's not surprising that it's also the main difference between indie and AAA game development. In fact, the difference in team or company size leads to every difference between the two game development processes. Let's start with something personal, your role or position in the development team. Big teams usually have every position they need already filled. If they need someone to work on the game engine, they already have a engine programmer there. If they need someone to design a level, they already have a level designer working on it. In a big team, your role is already determined from the start, and you will rarely work on any task outside of your job description. If AAA game development values specialists, then indie game development values generalists who can fill multiple roles. It's not weird at all in a small development team if a programmer is asked to deal with both networking as well as enemy AI. Small teams usually aren't able to individually cover all the needed positions, so they turn to people who are able to work on a variety of tasks. Funding across the games industry Let's move to another difference, this time from the funding aspect. A large team requires a large amount of funding, simply because it has more people that need to be paid. And, if you look at the bigger picture, it also means that video games made by a large team have a large development cost. The opposite rings true as well; indie game development has much smaller development costs because they have smaller teams. Because every project has a chance of failure, the large development cost of AAA games becomes a big problem. If you're only spending a little money, maybe you're fine with a small chance of failure, but if you're spending a large sum of money, you definitely want to reduce that risk as much as possible. This ends up with AAA game development being much more risk-averse; they're trying to avoid risk as much as possible. In AAA game development, when there's a decision that needs to be made, the team will try to make sure that they don't make the wrong choice. They will do extensive market research and they will see what is trending in the market. They'd want to grab as many audience members as possible, so if there's any design that will exclude a significant amount of customers, it will be cut out. On the other hand, indie game development doesn't spend that much money. With a smaller development cost, indie games don't need to have a massive amount of sale to recoup their costs. Because of that, they're willing to take risks with experimental and unorthodox design, giving the team the creative freedom without needing to do market research. That said, indie game development harbors a different kind of risk. Unlike their bigger counterpart, indie game developers tend to live from one game to the next. That is, they use the revenue from their current game to fund the development of their next game. So if any of their games don't perform well, they could immediately close down. And that's another difference between the two game development process, AAA game development tends to be more financially stable compared to indie development. There are more differences between indie and AAA game development, but the ones listed above are definitely some of the most prominent. All in all, one development process isn't better than the other, and it falls back on you to decide which one is better suited for you. Raka Mahesa is a game developer at Chocoarts, who is interested in digital technology in general. Outside of work hours, he likes to work on his own projects, with Corridoom VR being his latest released game. Raka also regularly tweets as @legacy99.

Cloud computing has risen massively in terms of popularity in recent times. This is due to the way it reduces on-premise infrastructure cost and improves efficiency. Primarily, the cloud model has been divided into three major service categories: Infrastructure as a Service (IaaS) Platform as a Service (PaaS) Software as a Service (SaaS) We will discuss each of these instances in the following sections: The article is an excerpt taken from the book 'Cloud Analytics with Google Cloud Platform', written by Sanket Thodge. Infrastructure as a Service (IaaS) Infrastructure as a Service often provides the infrastructure such as servers, virtual machines, networks, operating system, storage, and much more on a pay-as-you-use basis. IaaS providers offer VM from small to extra-large machines. The IaaS gives you complete freedom while choosing the instance type as per your requirements: Common cloud vendors providing the IaaS services are: Google Cloud Platform Amazon Web Services IBM HP Public Cloud Platform as a Service (PaaS) The PaaS model is similar to IaaS, but it also provides the additional tools such as database management system, business intelligence services, and so on. The following figure illustrates the architecture of the PaaS model: Cloud platforms providing PaaS services are as follows: Windows Azure Google App Engine Cloud Foundry Amazon Web Services Software as a Service (SaaS) Software as a Service (SaaS) makes the users connect to the products through the internet (or sometimes also help them build in-house as a private cloud solution) on a subscription basis model. Below image shows the basic architecture of SaaS model. Some cloud vendors providing SaaS are: Google Application Salesforce Zoho Microsoft Office 365 Differences between SaaS, PaaS, and IaaS The major differences between these models can be summarized to a table as follows: Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Software as a service is a model in which a third-party provider hosts multiple applications and lets customers use them over the internet. SaaS is a very useful pay-as-you-use model. Examples: Salesforce, NetSuite This is a model in which a third-party provider application development platform and services built on its own infrastructure. Again these tools are made available to customers over the internet. Examples: Google App Engine, AWS Lambda In IaaS, a third-party application provides servers, storage, compute resources, and so on. And then makes it available for customers for their utilization. Customers can use IaaS to build their own PaaS and SaaS service for their customers. Examples: Google Cloud Compute, Amazon S3 How PaaS, IaaS, and SaaS are separated at a service level In this section, we are going to learn about how we can separate IaaS, PaaS, and SaaS at the service level: As the previous diagram suggests, we have the first column as OPS, which stands for operations. That means the bare minimum requirement for any typical server. When we are going with a server to buy, we should consider the preceding features before buying. It includes Application, Data, Runtime, Framework, Operating System, Server, Disk, and Network Stack. When we move to the cloud and decide to go with IaaS—in this case, we are not bothered about the server, disk, and network stack. Thus, the headache of handling hardware part is no more with us. That's why it is called Infrastructure as a Service. Now if we think of PaaS, we should not be worried about runtime, framework, and operating system along with the components in IaaS. Things that we need to focus on are only application and data. And the last deployment model is SaaS—Software as a Service. In this model, we are not concerned about literally anything. The only thing that we need to work on is the code and just a look at the bill. It's that simple! If you found the above excerpt useful, make sure to check out the book 'Cloud Analytics with Google Cloud Platform' for more of such interesting insights into Google Cloud Platform. Read more Top 5 cloud security threats to look out for in 2018 Is cloud mining profitable? Why AWS is the prefered cloud platform for developers working with big data?

The most common programming languages currently used for AI and machine learning development are Python, R, Scala, Go, among others with the latest addition being Julia. Functional languages as old as Lisp and Haskell were used to implement machine learning algorithms decades ago when AI was an obscure research area of interest. There wasn’t enough hardware and software advancements back them for implementations. Some commonalities in all of the above language options are that they are simple to understand and promote clarity. They use fewer lines of code and lend themselves well to the functional programming paradigm. What is Functional programming? Functional programming is a programming approach that uses logical functions or procedures within its programming structure. It means that the programming is done with expressions instead of statements. In a functional programming (FP) approach, computations are treated as evaluations of mathematical functions and it mostly deals with immutable data. In other words, the state of the program does not change, the functions or procedures are fixed and the output value of a function depends solely on the arguments passed to it. Let’s look at the characteristics of a functional programming approach before we see why they are well suited for developing artificial intelligence programs. Functional programming features Before we see functional programming in a machine learning context, let’s look at some of its characteristics. Immutable: If a variable x is declared and used in the program, the value of the variable is never changed later anywhere in the program. Each time the variable x is called, it will return the same value assigned originally. This makes it pretty straightforward, eliminating the need to think of state change throughout the program. Referential transparency: This means that an expression or computation always results in the same value in any part/context of the program. A referentially transparent programming language’s programs can be manipulated as algebraic equations. Lazy evaluation: Being referentially transparent, the computations yield the same result irrespective of when they are performed. This enables to postpone the computation of values until they are required/called. This means one could evaluate them lazily. Lazy evaluation helps avoids unnecessary computations and saves memory. Parallel programming: Since there is no state change due to immutable variables, the functions in a functional program can work in parallel as instructions. Parallel loops can be easily expressed with good reusability. Higher-order functions: A higher order function can take one or more functions as arguments. They may also be able to return a function as their result. Higher-order functions are useful for refactoring code and to reduce repetition. The map function found in many programming languages is an example of a higher-order function. What kind of programming is good for AI development? Machine learning is a sub-domain of artificial intelligence which deals with concepts of making predictions from data, take actions without being explicitly programmed, recommendation systems and so on. Any programming approach that focuses on logic and mathematical functions is good for artificial intelligence (AI). Once the data is collected and prepared it is time to build your machine learning model.. This typically entails choosing a model, then training and testing the model with the data. Once the desired accuracy/results are achieved, then the model is deployed. Training on the data requires data to be consistent and the code to be able to communicate directly with the data without much abstraction for least unexpected errors. For AI programs to work well, the language needs to have a low level implementation for faster communication with the processor. This is why many machine learning libraries are created in C++ to achieve fast performance. OOP with its mutable objects and object creation is better suited for high-level production software development, not very useful in AI programs which works with algorithms and data. As AI is heavily based on math, languages like Python and R are widely used languages in AI currently. R lies more towards statistical data analysis but does support machine learning and neural network packages. Python being faster for mathematical computations and with support for numerical packages is used more commonly in machine learning and artificial intelligence. Why is functional programming good for artificial intelligence? There are some benefits of functional programming that make it suitable for AI. It is closely aligned to mathematical thinking, and the expressions are in a format close to mathematical definitions. There are few or no side-effects of using a functional approach to coding, one function does not influence the other unless explicitly passed. This proves to be great for concurrency, parallelization and even debugging. Less code and more consistency The functional approach uses fewer lines of code, without sacrificing clarity. More time is spent in thinking out the functions than actually writing the lines of code. But the end result is more productivity with the created functions and easier maintenance since there are fewer lines of code. AI programs consist of lots of matrix multiplications. Functional programming is good at this just like GPUs. You work with datasets in AI with some algorithms to make changes in the data to get modified data. A function on a value to get a new value is similar to what functional programming does. It is important for the variables/data to remain the same when working through a program. Different algorithms may need to be run on the same data and the values need to be the same. Immutability is well-suited for that kind of job. Simple approach, fast computations The characteristics/features of functional programming make it a good approach to be used in artificial intelligence applications. AI can do without objects and classes of an object oriented programming (OOP) approach, it needs fast computations and expects the variables to be the same after computations so that the operations made on the data set are consistent. Some of the popular functional programming languages are R, Lisp, and Haskell. The latter two are pretty old languages and are not used very commonly. Python can be used as both, functional and object oriented. Currently, Python is the language most commonly used for AI and machine learning because of its simplicity and available libraries. Especially the scikit-learn library provides support for a lot of AI-related projects. FP is fault tolerant and important for AI Functional programming features make programs fault tolerant and fast for critical computations and rapid decision making. As of now, there may not be many such applications but think of the future, systems for self-driving cars, security, and defense systems. Any fault in such systems would have serious effects. Immutability makes the system more reliable, lazy evaluation helps conserve memory, parallel programming makes the system faster. The ability to pass a function as an argument saves a lot of time and enables more functionality. These features of functional programming make it a fitting choice for artificial intelligence. To further understand why use functional programming for machine learning, read the case made for using the functional programming language Haskell for AI in the Haskell Blog. Why functional programming in Python matters: Interview with best selling author, Steven Lott Grain: A new functional programming language that compiles to Webassembly 8 ways Artificial Intelligence can improve DevOps

Men in rags became millionaires and rich people bite the dust within minutes, thanks to crypto currencies. According to a research, over 1500 crypto currencies are being traded globally and with over 6 million wallets, proving that digital currency is here not just to stay but to rule. The rise and fall of crypto market isn’t hidden from anyone but the catch is—cryptocurrency still sells like a hot cake. According to Bill Gates, “The future of money is digital currency”. With thousands of digital currencies rolling globally, crypto traders are immensely occupied and this is where cryptocurrency trading bots come into play. They ease out the currency trade and research process that results in spending less effort and earning more money not to mention the hours saved. According to Eric Schmidt, ex CEO of Google, “Bitcoin is a remarkable cryptographic achievement and the ability to create something that is not duplicable in the digital world has enormous value.” The crucial part is - whether the crypto trading bot is dependable and efficient enough to deliver optimum results within crunch time. To make sure you don't miss an opportunity to chip in cash in your digital wallet, here are the top 15 crypto trading bots ranked according to the performance: 1- Gunbot Gunbot is a crypto trading bot that boasts of detailed settings and is fit for beginners as well as professionals. Along with making custom strategies, it comes with a“Reversal Trading” feature. It enables continuous trading and works with almost all the exchanges (Binance, Bittrex, GDAX, Poloniex, etc). Gunbot is backed by thousands of users that eventually created an engaging and helpful community. While Gunbot offers different packages with price tags of 0.02 to 0.15 BTC, you can always upgrade them. The bot comes with a lifetime license and is constantly upgraded. Haasbot Hassonline created this cryptocurrency trading bot in January 2014. Its algorithm is very popular among cryptocurrency geeks. It can trade over 500 altcoins and bitcoins on famous exchanges such as BTCC, Kraken, Bitfinex, Huobi, Poloniex, etc. You need to put a little input of the currency and the bot will do all the trading work for you. Haasbot is customizable and has various technical indicator tools. The cryptocurrency trading bot also recognizes candlestick patterns. This immensely popular trading bot is priced between 0.12BTC and 0.32 BTC for three months. 3- Gekko Gekko is a cryptocurrency trading bot that supports over 18 Bitcoin exchanges including Bitstamp, Poloniex, Bitfinex, etc. This bot is a backtesting platform and is free for use. It is a full fledged open source bot that is available on the GitHub. Using this bot is easy as it comes with basic trading strategies. The webinterface of Gekko was written from scratch and it can run backtests, visualize the test results while you monitor your local data with it. Gekko updates you on the go using plugins for Telegram, IRC, email and several different platforms. The trading bot works great with all operating systems such as Windows, Linux and macOS. You can even run it on your Raspberry PI and cloud platforms. 4- CryptoTrader CyrptoTrader is a  cloud-based platform which allows users to create automated algorithmic trading programs in minutes. It is one of the most attractive crypto trading bot and you wont need to install any unknown software with this bot. A highly appreciated feature of CryptoTrader is its Strategy Marketplace where users can trade strategies. It supports major currency exchanges such as Coinbase, Bitstamp, BTCe and is supported for live trading and backtesting. The company claims its cloud based trading bots are unique as compared with the currently available bots in the market. 5- BTC Robot One of the very initial automated crypto trading bot, BTC Robot offers multiple packages for different memberships and software. It provides users with a downloadable version of Windows. The minimum robot plan is of $149. BTC Robot sets up quite easily but it is noted that its algorithms aren't great at predicting the markets. The user mileage in BTC Robot varies heavily leaving many with mediocre profits. With the trading bot’s fluctuating evaluation, the profits may go up or down drastically depending on the accuracy of algorithm. On the bright side the bot comes with a sixty day refund policy that makes it a safe buy. 6- Zenbot Another open source trading bot for bitcoin trading, Zenbot can be downloaded and its code can be modified too. This trading bot hasn't got an update in the past months but still, it is among one of the few bots that can perform high frequency trading while backing up multiple assets at a time. Zenbot is a lightweight artificially intelligent crypto trading bot and supports popular exchanges such as Kraken, GDAX, Poloniex, Gemini, Bittrex, Quadriga, etc. Surprisingly, according to the GitHub’s page, Zenbot’s version 3.5.15 bagged an ROI of 195% in just a mere period of three months. 7- 3Commas 3Commas is a famous cryptocurrency trading bot that works well with various exchanges including Bitfinex, Binance, KuCoin, Bittrex, Bitstamp, GDAX, Huiboi, Poloniex and YOBIT. As it is a web based service, you can always monitor your trading dashboard on desktop, mobile and laptop computers. The bot works 24/7 and it allows you to take-profit targets and set stop-loss, along with a social trading aspect that enables you to copy the strategies used by successful traders. ETF-Like feature allows users to analyze, create and back-test a crypto portfolio and pick from the top performing portfolios created by other people. 8- Tradewave Tradewave is a platform that enables users to develop their own cryptocurrency trading bots along with automated trading on crypto exchanges. The bot trades in the cloud and uses Python to write the code directly in the browser. With Tradewave, you don't have to worry about the downtime. The bot doesn't force you to keep your computer on 24x7 nor it glitches if not connected to the internet. Trading strategies are often shared by community members that can be used by others too. However, it currently supports very few cryptocurrency exchanges such as Bitstamp and BTC-E but more exchanges will be added in coming months. 9- Leonardo Leonardo is a cryptocurrency trading bot that supports a number of exchanges such as Bittrex, Bitfinex, Poloniex, Bitstamp, OKCoin, Huobi, etc. The team behind Leonardo is extremely active and new upgrades including plugins are in the funnel. Previously, it cost 0.5 BTC but currently, it is available for $89 with a license of single exchange. Leonardo boasts of two trading strategy bots including Ping Pong Strategy and Margin Maker Strategy. The first strategy enables users to set the buy and sell price leaving all of the other plans to the bot while the Margin Maker strategy can buy and sell on price adjusted according to the direction in the market. This trading bot stands out in terms of GUI. 10- USI Tech USI Tech is a trading bot that is majorly used for forex trading but it also offers BTC packages. While majority of trading bots require an initial setup and installation, USI uses a different approach and it isn't controlled by the users. Users are needed to buy-in from their expert mining and bitcoin trade connections and then, the USI Tech bot guarantees a daily profit from the transactions and trade. To earn one percent of the capital daily, customers are advised to choose feature rich plans.. 11- Cryptohopper Cryptohopper  is a 24/7 cloud based trading bot that means it doesn't matter  if you are on the computer or not. Its system enables users to trade on technical indicators with subscription to a signaler who sends buy signals. According to the Cryptohopper’s website, it is the first crypto trading bot that is integrated with professional external signals. The bot helps in leveraging bull markets and has a latest dashboard area where users can monitor and configure everything. The dashboard also includes a configuration wizard for the major exchanges including Bittrex, GDAX, Kraken,etc. 12- My Bitcoin Bot MBB is a team effort from Brad Sheridon and his proficient teammates who are experts of cryptocurrency investment. My Bitcoin Bot is an automated trading software that can be accessed by anyone who is ready to pay for it. While the monthly plan is of $39 a month, the yearly subscription for this auto-trader bot is available for of $297. My bitcoin bot comes with heaps of advantages such as unlimited technical support, free software updates, access to trusted brokers list, etc. 13- Crypto Arbitrager A standalone application that operates on a dedicated server, Crypto Arbitrager can leverage robots even when the PC is off. The developers behind this cryptocurrency trading bot claim that this software uses code integration of financial time series. Users can make money from the difference in rates of Litecoins and Bitcoins. By implementing the advanced strategy of hedge funds, the trading bot effectively manages savings of users regardless of the state of the cryptocurrency market. 14- Crypto Robot 365 Crypto Robot 365 automatically trades your digital currency. It buys and sells popular cryptocurrencies such as Ripple, Bitcoin, ethereum, Litecoin, Monero, etc. Rather than a signup fee, this platform charges its commision on a per trade basis. The platform is FCA-Regulated and offers a realistic achievable win ratio. According to the trading needs, users can tweak the system. Moreover, it has an established trading history and  it even offers risk management options. Down The Line While cryptocurrency trading is not a piece of cake, trading with currency bots may be confusing for many. The aforementioned trading bots are used by many and each is backed by years of extensive hard work. With reliability, trustworthiness, smartwork and proactiveness being top reasons for choosing any cryptocurrency trading bot, picking up a trading bot is a hefty task. I recommend you experiment with small amount of money first and if your fate gets to a shining start, pick the trading bot that perfectly suits your way of making money via cryptocurrency. About the Author Rameez Ramzan is a Senior Digital Marketing Executive of Cubix - mobile app development company.  He specializes in link building, content marketing, and site audits to help sites perform better. He is a tech geek and loves to dwell on tech news. Crypto-ML, a machine learning powered cryptocurrency platform Beyond the Bitcoin: How cryptocurrency can make a difference in hurricane disaster relief Apple changes app store guidelines on cryptocurrency mining

The augmented reality market is developing rapidly. Today, it has a total market value of almost $15 billion; according to Statista,  and this figure could rise to $210 billion by 2022. Augmented reality is having a huge impact on the games industry, but it’s being used by organizations in fields as diverse as publishing and retail.. For example, Layar is an app that turns static objects into live objects, while IKEA’s Catalog app lets you imagine how different types of furniture might fit into your room. But it’s not just about commerce: some apps have a distinctly educational bent, like Field Trip. Field Trip uses augmented reality to help users learn about the history that immediately surrounds them. The best augmented reality apps are always deceptively simple. But to build a really effective augmented reality application you need a diverse range of skills, that span both the domains of software and real-world physics. Let’s take a closer look at location-based augmented reality apps, including what they’re used for and how you can begin building them. How does location-based AR app work? Location-based augmented reality apps are sometimes called geo-based AR apps. Whatever you call them, one thing is important: they collate GPS mobile data and the digital compass to detect the location and position of the device. The application works like this: The AR app arranges queries to be dispatched to the sensor. Once the data has been acquired, the app can determine where it should add virtual information (such as images) should be added to the real world. Location-based augmented reality apps can be used both inside or outside. When inside and it isn’t possible to connect to GPS, the application will use beacons for location data. The best examples of existing location-based augmented reality apps While reading about location-based augmented reality apps can give you a good idea of how they work, to be really inspired, you need to try some out for yourself. Here’s a list of some of the best location-based augmented reality apps out there. Yelp Monocle Yelp Monocle helps you navigate an unknown city. Using GPS, it provides exactly the sort of information you’d expect from Yelp, but in a format that’s fully integrated with your surroundings. So, you can see restaurant reviews, shop opening hours as you move around your environment. Ingress Ingress is an augmented reality gaming app that immerses you in a (semi) virtual world. Your main mission is to find portals that the game ‘creates’ in your immediate environment and open them. Essentially, the game is a great way to explore the world around you and places a new augmented layer on a place that might otherwise be familiar. Vortex Planetarium Vortex Planetarium is an app for aspiring astronomers or anybody else with a passing interest in astronomy. The app detects the user’s location and then provides them with celestial data to better understand the night sky. Steps to create location-based AR app So, if you like the idea of a location-based augmented reality app, you’ll probably want to get started. As we’ve seen, these apps can be incredibly complex, but if you break the development process down, it should become much easier. 1. Determine what resources you need Depending on the complexity of your app, you need to determine what resources are needed - that could be anything from data to other frameworks and services will be required. For example, if you plan to create a game with 3D objects, you’ll need to use Unity to build in that level of functionality and realism. 2. Choose the right augmented reality tool There are a huge number of available augmented reality software development kits out there. However, rather than wade through every single one, here are some of the best to get started with. R SDKs, but we will list the most popular ones that can give you the widest range of possible features. AR Kit by Apple AR kit from Apple features just about everything you’d need to develop an augmented reality application, For example, it has a technology that allows combines both computer vision and camera data to track the user’s environment. AR Kit also is able to adjust the light level in the virtual model, to respond to the level of light in the real world. ARKit 2 recently brought users a number of cool new features. For example, it allows you to build interactivity into your application, and also allows you to build ‘memory’ into your app so it can ‘remember’ the location of augmented reality objects.ARCore by Google In Google’s ARCore you’ll find a mapping tool which is particularly useful for developing of location-based AR apps. ARCore can also track motion and detect vertical and horizontal surfaces. In the latest version of ARCore users can take two gadgets and work with one AR object from different viewing angles. 3. Geolocation data should be added Not all SDKs provide mapping feature. If it doesn’t, it’s essential to make sure you add in geolocation data. Without it, the app wouldn’t work! As we’ve already seen, GPS technology is typically used. It’s convenient and it can detect a user's location anywhere. It can, however, consume a lot of energy. Location services on iOS and Android will help to activate geolocation on the device. 3 augmented reality pitfalls to avoid Developing something as complex as a location-based augmented reality app is bound to lead to some challenges. So be prepared - watch for some of these pitfalls.. Ensure you have proper functionality. When users move with their camera and look for AR objects, these objects should remain static, regardless of the user’s movements. To do this, use SLAM - Simultaneous Localization and Mapping. This is a technique that allows software systems - like robots - ‘understand’ where they are situated in relation to their surroundings. Accuracy. A crucial factor for any AR app is accuracy. When developing your app, it’s essential to consider the user’s position to ensure that the app sends queries to sensors correctly. If it doesn’t the whole experience could seem plain weird for the user. Similarly, the distance between the device and the real world must be calculated correctly - again, if it isn’t your application simply will not work. Get started - build an awesome augmented reality app! Clearly, building a location-based augmented reality app isn’t easy. It requires skill and a commitment to keep going in the face of challenges. You certainly need a team of great developers around you if you’re going to deliver something that makes an impact. But, really, that’s what makes software development exciting, right? Author Bio Vitaly Kuprenko is a technical writer at Cleveroad. It's a web and mobile app development company in Ukraine. He enjoys telling about tech innovations and digital ways to boost businesses. Magic Leap unveils Mica, a human-like AI in augmented reality. Magic Leap teams with Andy Serkis’ Imaginarium Studios to enhance Augmented Reality “As Artists we should be constantly evolving our technical skills and thought processes to push the boundaries on what’s achievable,” Marco Matic Ryan, Augmented Reality Artist

Now that you’ve opened this article, I’ll assume you’re a web developer who is all excited with the prospect of building a machine learning project. You may be here for one of these reasons. Either you have been in a circle of people who find web development is dying? (Is it really dying or just unwell?). Or maybe you are stagnating in your current trajectory. And so, you want to learn something different, something trending, something like Artificial Intelligence. Or you/your employer/your client is aware of the capabilities of machine learning and want to include it in some part of your web app to make it more powerful. Or like the majority of the folks, you just want to see first hand if all the fuss about artificial intelligence is really worth all the effort to switch gears, by building a side toy ML project. Either way, there are different approaches to fulfill these needs. Learning Machine Learning for the Web with Javascript Learning machine learning coming from a web development background comes with its own constraints. You might worry about having to learn entirely different concepts from scratch - from different algorithms to programming languages like Python to mathematical concepts like linear algebra, calculus, and statistics. However, chances are you can skip learning a new language. You probably know some Javascript in some form or the other thanks to your web development experience. As such, you can learn Machine Learning in JavaScript (You don’t have to learn another programming language from scratch) and take it right to your browsers with WebGL. There are some advantages to using JavaScript for ML. Its popularity is one; while ML in JavaScript is not as popular as Python’s ML ecosystem, at the moment, the language itself is. As demand for ML applications rises, and as hardware becomes faster and cheaper, it's only natural for machine learning to become more prevalent in the JavaScript world. The JavaScript ecosystem offers a rich set of libraries suited for most Machine Learning tasks. Math: math.js Data Analysis: d3.js Server: node.js (express, koa, hapi) Performance: Tensorflow.js (e.g. GPU accelerated via WebGL API in the browser), Keras.js etc. Read also: 5 JavaScript machine learning libraries you need to know BRIIM is a good collection of materials to get you started as web developer or JavaScript enthusiast in machine learning. In case you’re interested in learning Python instead of Javascript, here are the set of libraries you should pick. Math: numpy Data Analysis: Pandas Data Mining: PySpark Server: Flask, Django Performance: TensorFlow (because it is written with a Python API over a C/C++ engine) or Keras (sits on top of TensorFlow). Using Machine Learning as a service If you don’t want to spend your time learning frameworks, tools, and languages suited for machine learning, you can adopt Machine Learning as a service or MLaaS. These services provide machine learning tools as part of cloud computing services. So basically, you can benefit from machine learning without the allied cost, time and risk of establishing an in-house internal machine learning team. All you need is sufficient knowledge of incorporating APIs. All Machine Learning tasks including data pre-processing, model training, model evaluation, and predictions can be completed through MLaaS. Read also: How machine learning as a service is transforming cloud A large number of companies provide Machine Learning as a service. Most prominent ones include: Amazon Machine Learning Amazon ML makes it easy for web developers to build smart applications using simple APIs. This includes applications for fraud detection, demand forecasting, targeted marketing, and click prediction. They provide a Developer Guide, which provides a conceptual overview of Amazon ML and includes detailed instructions for using the service. They also have a API reference, which describes all the API operations and provides sample requests and responses for supported web service protocols. Azure ML web app templates The web app templates available in the Azure Marketplace can build a custom web app that knows your web service's input data and expected results. All you need to do is give the web app access to your web service and data, and the template does the rest. There are two available templates: Azure ML Request-Response Service Web App Template Azure ML Batch Execution Service Web App Template Each template creates a sample ASP.NET application by using the API URI and key for your web service. The template then deploys the application as a website to Azure. No coding is required to use these templates. You just supply the API key and URI, and the template builds the application for you. Google Cloud based APIs Google also provides machine learning services, with pre-trained models and a service to generate your own tailored models. Google’s Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. Cloud AutoML is used by Disney on their website shopDisney to enhance guest experience through more relevant search results, expedited discovery, and product recommendations. Building Conversational Interfaces As a web developer, another thing you might be looking into, is developing conversational interfaces or chatbots to enhance your web apps. Amazon, Google, and Microsoft provide Machine learning powered tools to help developers with building their own chatbots. Amazon Lex You can embed chatbots in your web apps with the Amazon Lex featuring ASR (Automatic Speech Recognition) and NLP (Natural Language Processing) capabilities. The API can recognize written and spoken text and the Lex interface allows you to hook the recognized inputs to various back-end solutions. Lex currently supports deploying chatbots for Facebook Messenger, Slack, and Twilio. Google Dialogflow Google’s Dialogflow can build voice and text-based conversational interfaces, such as voice apps and chatbots, powered by AI. Dialogflow incorporates Google's machine learning expertise and products such as Google Cloud Speech-to-Text. The API can be tweaked and customized for needed intents using Java, Node.js, and Python. It is also available as an enterprise edition. Microsoft Azure Cognitive Services Microsoft Cognitive Services simplify a variety of AI-based tasks, giving you a quick way to add intelligence technologies to your bots with just a few lines of code. It provides tools and APIs for aiding the development of conversational interfaces. These include: Translator Speech API Bing Speech API to convert text into speech and speech into text Speaker Recognition API for voice verification tasks Custom Speech Service to apply Azure NLP capacities using own data and models Language Understanding Intelligent Service (LUIS) is an API that analyzes intentions in text to be recognized as commands Text Analysis API for sentiment analysis and defining topics Bing Spell Check Translator Text API Web Language Model API that estimates probabilities of words combinations and supports word autocompletion Linguistic Analysis API used for sentence separation, tagging the parts of speech, and dividing texts into labeled phrases Read also: Top 4 chatbot development frameworks for developers These tools should be enough to get your feet off the ground quickly and move into the specific area of machine learning. Ultimately your choice of tool relies on the kind of application you want to build, your level of expertise, and how much time and effort you’re willing to put to learn. Obviously, depending on your area of choice, you would have to do more research and develop yourself in those areas. How should web developers learn machine learning? 5 examples of Artificial Intelligence in Web apps The most valuable skills for web developers to learn in 2018

Apple recently announced a new declarative UI framework for its operating system - SwiftUI, at its annual developer conference WWDC 2019. SwiftUI will power all of Apple’s devices (MacBooks, watches, tv’s, iPads and smartphones). You can integrate SwiftUI views with objects from the UIKit, AppKit, and WatchKit frameworks to take further advantage of platform-specific functionality. It's said to be productive for developers and would save effort while writing codes. SwiftUI documentation,  states that, “Declare the content and layout for any state of your view. SwiftUI knows when that state changes, and updates your view’s rendering to match.”   This means that the developers simply have to describe the current UI state to the response of events and leave the in-between transitions to the framework. The UI updates the state automatically as it changes. Benefits of a Declarative UI language Without describing the control flow, the declarative UI language expresses the logic of computation. You describe what elements you need and how they would look like without having to worry about its exact position and its visual style. Some of the benefits of Declarative UI language are: Increased speed of development. Seamless integration between designers and coders. Forces separation between logic and presentation.    Changes in UI don’t require recompilation SwiftUI’s declarative syntax is quite similar to Google’s Flutter which also runs on declarative UI programming. Flutter contains beautiful widgets with captivating logos, fonts, and expressive style. The use of Flutter has significantly increased in 2019 and is among the fastest developing skills in the developer community. Similar to Flutter, SwiftUI provides layout structure, controls, and views for the application’s user interface. This is the first time Apple’s stepping up to the declarative UI programming and has described SwiftUI as a modern way to declare user interfaces. In the imperative method, developers had to manually construct a fully functional UI entity and later change it using methods and setters. In SwiftUI the application layout just needs to be described once, vastly reducing the code complexity. Apart from declarative UI, SwiftUI also features Xcode, which contains software development tools and is an integrated development environment for the OS.  If any code modifications are made inside Xcode, developers now can preview the codes in real-time and tweak parameters. Swift UI also features dark mode, drag and drop building tools by Xcode and interface layout.  Languages such as Hebrew and Arabic are also incorporated. However, one of the drawbacks of SwiftUI is that it will only support apps that will continue to relay forward with iOS13. It’s a sort of limited tool in this sense and the production would take at least a year or two if an older iOS version is to be supported. SwiftUI vs Flutter Development   Apple’s answer to Google is simple here. Flutter is compatible with both Android and iOS whereas SwiftUI is a new member of Apple’s ecosystem. Developers use Flutter for cross-platform apps with a single codebase. It highlights that Flutter is pushing other languages to adopt its simplistic way of developing UI. Now with the introduction of SwiftUI, which works on the same mechanism as Flutter, Apple has announced itself to the world of declarative UI programming. What does it mean for developers who build exclusively for iOS? Well, now they can make Native Apps for their client’s who do not prefer the Flutter way. SwiftUI will probably reduce the incentive for Apple-only developers to adopt Flutter. Many have pointed out that Apple has just introduced a new framework for essentially the same UI experience. We have to wait and see what Swift UI has under its closet for the longer run. Developers in communities like Reddit and others are actively sharing their thoughts on the recent arrival of SwiftUI. Many agree on the fact that “SwiftUI is flutter with no Android support”.   Developers who’d target “Apple only platform” through SwiftUI, will eventually return to Flutter to target all other platforms, which makes Flutter could benefit from SwiftUI and not the other way round. The popularity of the react native is no brainer. Native mobile app development for iOS and Android is always high on cost and companies usually work with 2 different sets of teams. Cross-platform solutions drastically bridge the gaps in terms of developmental costs. One could think of Flutter as React native with the full support of native features (one doesn’t have to depend on native platforms for solutions and Flutter renders similar performance to native). Like React Native, Flutter uses reactive-style views. However, while React Native transpiles to native widgets, Flutter compiles all the way to native code. Conclusion SwiftUI is about making development interactive, faster and easier. The latest inbuilt graphical UI design tool allows designers to assemble a user interface without having to write any code. Once the code is modified, it instantly appears in the visual design tool. Codes can be assembled, redefined and tested in real time with previews that could run on a range of Apple's devices. However, SwiftUI is still under development and will take its time to mature. On the other hand, Flutter app development services continue to deliver scalable solutions for startups/enterprises. Building native apps are not cheap and Flutter with the same feel of native provides cost-effective services. It still remains a competitive cross-platform network with or without SwiftUI’s presence. Author Bio Keval Padia is the CEO of Nimblechapps, a prominent Mobile app development company based in India. He has a good knowledge of Mobile App Design and User Experience Design. He follows different tech blogs and current updates of the field lure him to express his views and thoughts on certain topics.

From data to web development, Python has come to stand as one of the most important and most popular open source programming languages being used today. But whilst some see it as almost a new kid on the block, Python is actually older than both Java, R, and JavaScript. So what are the origins of our favorite open source language? In the beginning... Python's origins lie way back in distant December 1989, making it the same age as Taylor Swift. Created by Guido van Rossum (the Python community's Benevolent Dictator for Life) as a hobby project to work on during week around Christmas, Python is famously named not after the constrictor snake but rather the British comedy troupe Monty Python's Flying Circus. (We're quite thankful for this at Packt - we have no idea what we'd put on the cover if we had to pick for 'Monty' programming books!) Python was born out of the ABC language, a terminated project of the Dutch CWI research institute that van Rossum worked for, and the Amoeba distributed operating system. When Amoeba needed a scripting language, van Rossum created Python. One of the principle strengths of this new language was how easy it was to extend, and its support for multiple platforms - a vital innovation in the days of the first personal computers. Capable of communicating with libraries and differing file formats, Python quickly took off. Computer Programming for Everybody Python grew throughout the early nineties, acquiring lambda, reduce(), filter() and map() functional programming tools (supposedly courtesy of a Lisp hacker who missed them and thus submitted working patches), key word arguments, and built in support for complex numbers. During this period, Python also served a central role in van Rossum's Computer Programming for Everybody initiative. The CP4E's goal was to make programming more accessible to the 'layman' and encourage a basic level of coding literacy as an equal essential knowledge alongside English literacy and math skills. Because of Python's focus on clean syntax and accessibility, it played a key part in this. Although CP4E is now inactive, learning Python remains easy and Python is one of the most common languages that new would-be programmers are pointed at to learn. Going Open with 2.0 As Python grew in the nineties, one of the key issues in uptake was its continued dependence on van Rossum. 'What if Guido was hit by a bus?' Python users lamented, 'or if he dropped dead of exhaustion or if he is rubbed out by a member of a rival language following?' In 2000, Python 2.0 was released by the BeOpen Python Labs team. The ethos of 2.0 was very much more open and community oriented in its development process, with much greater transparency. Python moved its repository to SourceForge, granting write access to its CVS tree more people and an easy way to report bugs and submit patches. As the release notes stated, 'the most important change in Python 2.0 may not be to the code at all, but to how Python is developed'. Python 2.7 is still used today - and will be supported until 2020. But the word from development is clear - there will be no 2.8. Instead, support remains focused upon 2.7's usurping younger brother - Python 3. The Rise of Python 3 In 2008, Python 3 was released on an almost-unthinkable premise - a complete overhaul of the language, with no backwards compatibility. The decision was controversial, and born in part of the desire to clean house on Python. There was a great emphasis on removing duplicative constructs and modules, to ensure that in Python 3 there was one - and only one - obvious way of doing things. Despite the introduction of tools such as '2to3' that could identify quickly what would need to be changed in Python 2 code to make it work in Python 3, many users stuck with their classic codebases. Even today, there is no assumption that Python programmers will be working with Python 3. Despite flame wars raging across the Python community, Python 3's future ascendancy was something of an inevitability. Python 2 remains a supported language (for now). But as much as it may still be the default choice of Python, Python 3 is the language's future. The Future Python's userbase is vast and growing - it's not going away any time soon. Utilized by the likes of Nokia, Google, and even NASA for it's easy syntax, it looks to have a bright future ahead of it supported by a huge community of OS developers. Its support of multiple programming paradigms, including object-oriented Python programming, functional Python programming, and parallel programming models makes it a highly adaptive choice - and its uptake keeps growing.

A powerful backend solution is essential for building sophisticated mobile apps. In recent years, Firebase has emerged to prominence as a power-packed Backend-as-a-Solution (BaaS), thanks to its wide-ranging features and performance boosting elements. After being acquired in 2014 by Google, several of its features further got a performance boost. These features have made  Firebase quite a popular backend solution for app developers and other emerging IT sectors. Let us look at its 4 key benefits for cross-platform mobile app development. Unleashing the power of Google Analytics Google Analytics for Firebase is a completely free solution with unconstrained reporting on many aspects. The reporting feature allows you to evaluate client behavior, report on broken links, user interactions and all other aspects of user experience and user interface. The reporting helps developers make informed decisions while optimizing the UI and the app performance. The unmatched scale of reporting: Firebase analytics allows access to unlimited reports on as many as 500 different events. The developers can also create custom events for reporting as their need suits. Robust audience segmentation: The Firebase analytics also allows segmenting the app audience on different parameters and grounds. The integrated console allows segmenting the audience on the basis of device information, custom events, and user characteristics. Crash reporting to fix Bugs Firebase also helps to address performance issues of an app by fixing bugs right from its backend solution. It is also equipped with robust crash reporting feature. Its crash reporting helps to deliver intricate and detailed bug and crash reports to address all the coding errors in an app. The reporting feature is capable of grouping together the issues in different categories as per the characteristics of the problem. Here are some of the attributes of this reporting feature. Monitoring errors: It is capable of monitoring fatal errors for iOS apps and both fatal and non-fatal errors for Android apps. Generally, reports are initiated as per the impact caused by such errors on the user experience. Required data collection to fix errors: The reports also enlist all the details concerning the device in use, performance shortfalls and user scenarios concerning the erroneous events. According to the contributing factors and other similarities, the issues are grouped in different categories. Email alerts: It also allows sending email alerts as and when such issues or problems are detected. The configuration of error reporting: The error reporting can also be configured remotely to control who can access the reports and list of events that occurred before an event. It is free: Crash and bug reporting is free with Firebase. You don't need to pay a penny to access this feature. Synchronizing data with real-time database With Firebase you can sync the offline and online data through NoSQL database. This makes the application data available on both offline and online states of the app. This boosts collaboration on the application data in real time. Here are some of its benefits. Real-time: Unlike the so-called HTTP requests that work to update the data across interfaces, the Real-time Database of firebase syncs data with every change thus helping to reflect the change in real time across any device in use. Offline: As Firebase Real-time Database SDK helps save your data in local disk, you can always access the data offline. As and when connectivity is back, the changes are synced with the present state of the server. Access from multiple devices: The Firebase Real-time Database allows accessing application data from multiple devices and interfaces including mobile devices and web. Splitting and scaling your data: Thanks to Firebase Real-time Database, you can split your data across multiple databases within the same project and set rules for each database instances. Firebase is feature rich for futuristic app development In addition to the above, Firebase is fully empowered with a host of rich features required for building sophisticated and most feature-rich mobile apps. Let us have a look at some of the key features of Firebase that made it a reliable platform for cross-platform development. Hosting: The hosting feature of Firebase allows developers to update their contents in the Content Delivery Network (CDN) during production. Firebase offers full hosting support with a custom domain, Global CDN, and an automatically provided SSL Certificate. Authentication: Firebase backend service offers a powerful authentication feature. It comes equipped with simple SDKs and easy to use libraries to integrate authentication feature with any mobile app. Storage: Firebase storage feature is powered by Google Cloud Storage and allows users to easily download media files and visual contents. This feature is also helpful in making use of user-generated content. Cloud Messaging: With Cloud Messaging, a mobile app powered can easily send a message to users and indulge in real-time communication. Remote Configuration: This feature of Firebase allows developers to incorporate certain changes in the app remotely. Thanks to this, the changes are reflected in the existing version, and the user does not need to download the latest updated version. Test Lab: With Test lab, developers can easily test the app in all the devices listed in the Google data center. It can even do the testing without requiring any test code of the respective app. Notifications: This feature gives developers a console to manage and send user-focused custom notifications to the users. App Indexing: This feature allows developers to index the app in Google Search and achieve higher search ranks in app marketplaces like Play Store and App Store. Dynamic Links: Firebase also equips the app to create dynamic links or smart URLs to present the respective app across all digital platforms including social media, mobile app, web, email, and other channels. All the above-mentioned benefits and useful features that empower mobile app developers to create dynamic user experience helped Firebase achieve such unprecedented popularity among developers worldwide. No wonder, in a short time span it has become a very popular backend solution for so many successful cross-platform mobile apps. Some exemplary use cases of Firebases Here we have picked two use cases of Firebase, respectively for one relatively new and successful app and one leading app in its niche. Fabulous Fabulous is a unique app that trains users to dispose of bad habits and get used to good habits to ensure health and wellbeing. The app by customizing the onboarding process through Firebase managed to double the retention rate. The app could incorporate custom user experience for different groups of users as per their preference. Onefootball This leading mobile soccer app OneFootBall experienced more than 5% increase in user session time thanks to Firebase. The new backend solution powered by Firebase helped the game app engage the audience more efficiently than ever before. The custom contents created by this popular app can enjoy better traction with users thanks to higher engagement. Author Bio: Juned Ahmed works as an IT consultant at IndianAppDevelopers, a leading Mobile app development company which offers to hire app developers in India for mobile solutions. He has more than 10 years of experience in developing and implementing marketing strategies. How to integrate Firebase on Android/iOS applications natively. Build powerful progressive web apps with Firebase. How to integrate Firebase with NativeScript for cross-platform app development.

The primary target for businesses while working on mobile apps is the Android platform, thanks to the massive market share the mobile operating system holds. It’s popularity can be attributed to the fact that it is open source and is regular updated with new enhancements and features. Android devices generally tend to differ based on the mobile hardware features even when powered by the same version of the Android OS. This is why it is essential that when developing apps for Android, developers create mobile apps capable of targeting a diverse range of mobile devices running on different versions of Android OS. During the various stages of planning, developing and testing, developers need to focus comprehensively on the apps functionality, accessibility, usability, performance, and security so that users can be engaged despite their choice of device. Also, they also need to look for ways to make the apps deliver a more personalized user experience across the various devices an operating system. Furthermore, developers need to understand and find solutions to the common challenges involved in android app development. Common Challenges Android App Developers Face 1. Hardware Features The Android OS is unlike any other mobile operating system. For one thing, it is an open source system. Alphabet gives manufacturers the leeway to customize the operating system to their specific needs. Also, there are no regulations on the devices being released by the different manufacturers. As a result, you can find various Android devices with different hardware features running on the same Android version. Two smartphones running on Android latest ver, for example, may have different screen resolutions, camera, screen size, and other hardware structures. During android app development, developers need to account for all of this to ensure the application delivers a personalized experience to each user. 2. Lack of Uniform User Interface Design Rules Since Google is yet to release any standard UI (user interface) design rules or process for mobile app developers, most developers don’t follow any standard UI development rules or procedure. Because developers are creating custom UI interfaces in their preferred way, a lot of apps tend to function or look different across different devices. This diversity and incompatibility of the UI usually affects the user experience that the Android app directly delivers. Smart developers prefer to go for a responsive layout that’ll keep the UI consistent across different devices. Moreover, developers need to test the UI of the app extensively by combining emulators and real mobile devices. Designing a UI that makes the app deliver the same user experience across varying Android devices is one of the more daunting challenges developers face. 3. API Incompatibility A lot of developers make use of third-party APIs to enhance the functionality and interoperability of a mobile device. Unfortunately, not all third-party APIs available for Android app development are of high quality.. Some APIs were created for a particular Android version and will not work on devices running on a different version of the operating system. Developers usually have to come up with ways to make a single API work on all Android versions, a task they often find to be very challenging. 4. Security Flaws As previously mentioned, Android is an open source software, and because of that, manufacturers find it easy to customize Android to their desired specifications. However, this openness and the massive market size makes Android a frequent target for security attacks. There have been several instances where the security of millions of Android mobile devices have been affected by security flaws and bugs like mRST, Stagefright, FakeID, ‘Certifi-gate,’ TowelRoot and Installer Hijacking. Developers need to include robust security features in their applications and utilize the latest encryption mechanisms to keep user information secure and out of the hands of hackers. 5. Search Engine Visibility The latest data from Statista shows that Google Play Store contains a higher number of mobile apps. Additionally, a large number of Android users prefer free apps than paid apps which is why developers need to promote their mobile applications to increase their download numbers and employ application monetization options. The best way to promote the app to reach their target audience is to use comprehensive digital marketing strategies. Most developers make use of digital marketing professionals to promote their apps aggressively. 6. Patent Issues Google doesn’t implement any guidelines for the evaluation of the quality of new apps that are getting submitted to the Play Store. This lack of a quality assessment guideline causes a lot of patent-related issues for developers. Some developers, to avoid patent issues, have to modify and redesign their apps in the future. As per my personal experience, I have tried to cover general challenges faced by Android app developers. I’m sure keeping wary of these challenges would help developers to build successful apps in the most hassle free way. Author Bio Harnil Oza is the CEO of Hyperlink InfoSystem, one of the leading app development companies in New York, USA and India who deliver mobile solutions mainly on Android and iOS platform. He regularly contributes his knowledge on leading blogging sites. LEGO launches BrickHeadz Builder AR, a new and free Android app to bring bricks and toys to life How Android app developers can convert iPhone apps How to Secure and Deploy an Android App

[box type="info" align="" class="" width=""]We bring to you another guest post by Benjamin Rojogan on Logistic regression to aid healthcare sector in reducing patient readmission. Ben's previous post on ensemble methods to optimize machine learning models is also available for a quick read here.[/box] ER visits are not cheap for any party involved. Whether this be the patient or the insurance company. However, this does not stop some patients from being regular repeat visitors. These recurring visits are due to lack of intervention for problems such as substance abuse, chronic diseases and mental illness. This increases costs for everybody in the healthcare system and reduces quality of care by playing a role in the overflowing of Emergency Departments (EDs). Research teams at UW and other universities are partnering with companies like Kensci to figure out how to approach the problem of reducing readmission rates. The ability to predict the likelihood of a patient’s readmission will allow for targeted intervention which in turn will help reduce the frequency of readmissions. Thus making the population healthier and hopefully reducing the estimated 41.3 billion USD healthcare costs for the entire system. How do they plan to do it? With big data and statistics, of course. A plethora of algorithms are available for data scientists to use to approach this problem. Many possible variables could affect the readmission and medical costs. Also, there are also many different ways researchers might pose their questions. However, the researchers at UW and many other institutions have been heavily focused on reducing the readmission rate simply by trying to calculate whether a person would or would not be readmitted. In particular, this team of researchers was curious about chronic ailments. Patients with chronic ailments are likely to have random flare ups that require immediate attention. Being able to predict if a patient will have an ER visit can lead to managing the cause more effectively. One approach taken by the data science team at UW as well as the Department of Family and Community Medicine at the University of Toronto was to utilize logistic regression to predict whether or not a patient would be readmitted. Patient readmission can be broken down into a binary output: either the patient is readmitted or not. As such logistic regression has been a useful model in my experience to approach this problem. Logistic Regression to predict patient readmissions Why do data scientists like to use logistic regression? Where is it used? And how does it compare to other data algorithms? Logistic regression is a statistical method that statisticians and data scientists use to classify people, products, entities, etc. It is used for analyzing data that produces a binary classification based on one or many independent variables. This means, it produces two clear classifications (Yes or No, 1 or 0, etc). With the example above, the binary classification would be: is the patient readmitted or not? Other examples of this could be whether to give a customer a loan or not, whether a medical claim is fraud or not, whether a patient has diabetes or not. Despite its name, logistic regression does not provide the same output like linear regression (per se). There are some similarities, for instance, the linear model is somewhat consistent as you might notice in the equation below where you see what is very similar to a linear equation. But the final output is based on the log odds. Linear regression and multivariate regression both take one to many independent variables and produce some form of continuous function. Linear regression could be used to predict the price of a house, a person’s age or the cost of a product an e-commerce should display to each customer. The output is not limited to only a few discrete classifications. Whereas logistic regression produces discrete classifiers. For instance, an algorithm using logistic regression could be used to classify whether or not a certain stock price would be either >$50 a share or <$50 a share. Linear regression would be used to predict if a stock share would be worth $50.01, $50.02….etc. Logistic regression is a calculation that uses the odds of a certain classification. In the equation above, the symbol you might know as pi actually represents the odds or probability. To reduce the error rate, we should predict Y = 1 when p ≥ 0.5 and Y = 0 when p < 0.5. This creates a linear classifier, a boundary that when the coefficients β0 + x · β has a p value that is p < 0.5 then Y = 0. By generating coefficients that help predict the logit transformation, the method allows to classify for the characteristic of interest. Now that is a lot of complex math mumbo jumbo. Let’s try to break it down into simpler terms. Probability vs. Odds Let’s start with probability. Let’s say a patient has the probability of 0.6 of being readmitted. Then the probability that the patient won’t be readmitted is .4. Now, we want to take this and convert it into odds. This is what the formula above is doing. You would take .6/.4 and get odds of 1.5. That means the odds of the patient being readmitted are 1.5 to 1. If instead the probability was .5 for both being readmitted and not being readmitted, then the odds would be 1:1. Now the next step in the logistic regression model would be to take the odds and get the “Log odds”. You do this by taking the 1.5 and put it into the log portion of the equation. Now you will get .18(rounded). In logistic regression, we don’t actually know p. That is what we are trying to essentially find and model using various coefficients and input variables. Each input provides a value that changes how much more likely an event will or will not occur. All of these coefficients are used to calculate the log odds. This model can take multiple variables like age, sex, height, etc. and specify how much of an effect each variable has on the odds an event will occur. Once the initial model is developed, then comes the work of deciding its value. How does a business go from creating an algorithm inside a computer and translate it into action. Some of us like to say the “computers” are the easy part. Personally I find the hard part to be the “people”. After all, at the end of the day, it comes down to business value. Will an algorithm save money or not? That means it has to be applied in real life. This could take the form of a new initiative, strategy, product recommendation, etc. You need to find the outliers that are worth going after! For instance, if we go back to the patient readmission example again. The algorithm points out patients with high probabilities of being readmitted. However if the readmission costs are low, they will probably be ignored..sadly. That is how businesses (including hospitals) look at problems. Logistic regression is a great tool for binary classification. It is unlike many other algorithms that estimate continuous variables or estimate distributions. This statistical method can be utilized to classify whether a person will be likely to get cancer because of environmental variables like proximity to a highway, smoking habits, etc? This method has been used effectively in the medical, financial and insurance industry successfully for a while. Knowing when to use what algorithm takes time. However, the more problems a data scientist faces, the faster they will recognize whether to use logistic regression or decision trees. Using logistic regression provides the opportunity for healthcare institutions to accurately target at risk individuals who should receive a more tailored behavioral health plan to help improve their daily health habits. This in turn opens the opportunity for better health for patients and lower costs for hospitals. [box type="shadow" align="" class="" width=""] About the Author Benjamin Rogojan Ben has spent his career focused on healthcare data. He has focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. He has also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. Ben privately consults on data science and engineering problems both solo as well as with a company called Acheron Analytics. He has experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.[/box]

Now that ES6 has been officially accepted, it’s time to look forward to the next iteration of JavaScript, which is ECMAScript 7. There are many new and exciting features in ES7. Support for asynchronous programming Of all the new features in ES7, the most exciting one, in my view, is the addition of async and await for asynchronous programming, which occurs quite often, especially when you're trying to build applications using Node.js. To explain async and await, it's better you first see an example. Let’s say you have three asynchronous operations, each one dependent on the result returned by the previous one. There are multiple ways you could do that. The most common way to do this is to utilize callbacks. Let’s take a look at the code: myFirstOperation(function(err, firstResult){ mySecondOperation(firstResult, function(err, secondResult){ myThirdOperation(secondResult, function(err, thirdResult){ /* Do something with the third result */ }); }); }); The obvious flaw with this approach is that it leads to a situation known as callback hell. The introduction of promises simplified async programming greatly, so let’s see how the code would look using promises (which were introduced with ES6): myFirstPromise() .then(firstResult => mySecondPromise(firstResult)) .then(secondResult => myThirdPromis(secondResult)) .then(thirdResult =>{ /* Do something with thrid result */ }, err => { /* Handle error */ }); Now, let’s see how to handle these operations using async and await: async function myOperations(){ const firstResult = await myFirstOperation(); const secondResult = await mySecondOperation(firstResult); const thirdResult = await myThirdOperation(secondResult); /* Do something with third result */ }; try { myOperations(); } catch (err) { /* Handle error */ } This looks just like synchronous code? What? Exactly! The use of async and await makes life much simpler, by making async functions seem as if they are synchronous code. Under the hood, though, all of these functions execute in a nonblocking fashion, so you have the benefit of nonblocking async functions, with the simplicity and readability of synchronous code. Brilliant! Object rest and Object spread In ES6, we saw the introduction of array rest and spread operations. These new additions make it easier for you to combine and decompose arrays. ES7 takes this one level further by providing similar functionality for objects. Object rest This is a extension to the existing ES6 destructuring operation. On assignment of the properties during destructuring, if there is an additional ...rest parameter, all the remaining keys and values are assigned to it as another object. For example: const myObject = { lorem : 'ipsum', dolor : 'sit', amet : 'foo', bar : 'baz' }; const { lorem, dolor, ...others } = myObject; // lorem === 'ipsum' // dolor === 'sit' // others === { amet : 'foo', bar : 'baz' } Object spread This is similar to object rest, but is used for constructing objects instead of destructuring them: const obj1 = { amet : 'foo', bar : 'baz' }; const myObject = { lorem : 'ipsum', dolor : 'sit', ...obj1 }; /* myObject === { lorem : 'ipsum', dolor : 'sit', amet : 'foo', bar : 'baz' }; */ This is an alternative way of expressing the Object.assign function already present in ES6. In the precding code, myObject, is a new object, constructed using some properties of obj1 (there is no reference to obj). The equivalent way of doing this in ES6 would be: const myObject = Object.assign({ lorem : 'ipsum', dolor : 'sit' }, obj1); Of course, the object spread notation is much more readable, and the recommended way of assigning new objects, if you choose to adopt it. Observables The Object.observe function is a great new addition for asynchronously monitoring changes made to objects. Using this feature, you will be able to handle any sort of change made to objects, along with seeing how and when that change was made. Let's look at an example of how Object.observe will work: const myObject = {}; Object.observe(myObject, (changes) => { const [{ name, object, type, oldValue }] = changes; console.log(`You tried to ${type} the ${name} property`); }); myObject.foo = 'bar'; //You tried to add the foo property Caveat Although this is a good feature, as of this writing, Object.observe is being tagged as obsolete, which means that this feature could be removed at any time in the future. While it’s still ok to play around and experiment with this, it is recommended not to use it in production systems and larger applications. Additional utility methods There have been additional methods added to the String and Array prototypes: Array.prototype.includes: This checks whether an array includes an element or not: [1,2,3].includes(1); //true String.prototype.padLeft and String.prototype.padRight: 'abc'.padLeft(10); //"abc " 'abc'.padRight(10); //" abc" String.prototype.trimLeft and String.prototype.trimRight: 'n t abc n t'.trimLeft(); //"abc n t" 'n t abc n t'.trimRight(); //"n t abc" Working with ES7 today Many of the features mentioned here are still in the proposal phase, but you can still get started using them in your JavaScript application today! The most common tool used to get started is babel. In case you want to make a browser application, babel is perfect for compiling all of your code to regular ES5. Alternatively, you can use the many babel plugins already available to use babel with your favorite toolbelt or build system. In case you have trouble setting up your project, there are many yeoman generators to help you get started. If you are planning to use ES7 to build a node module or an application in node, there is a yeoman generator available for that as well. About the author Soham Kamani is a Full stack web developer and electronics hobbyist. He is especially interested in JavaScript, Python, and IOT. He can be found on Twitter at @sohamkamani and at sohamkamani.com.

Today, we are going to be discussing some of the oldest, most established programming languages that are still in use today. Some developers may be surprised to learn that many of these languages surpass them in age, in a world where technology, especially in the world of development, is advancing at such a rapid rate. But then, old is gold, after all. So, in age order, let’s present the oldest programming languages in use today: C The C language was created in 1972 (it’s not that old, okay). C is a lower level language that was based an earlier language called B (do you see a trend here?) It is a general-purpose language, and a parent language which many future programming languages derive from, such as C#, Java, JavaScript, Perl, PHP and Python. It is used in many applications that must interface with hardware or play with memory. C++ Pronounced see-plus-plus, C++ was developed 11 years later in 1983. It is very similar to C, in fact it is often considered an extension of C. It added various concepts such as classes, virtual functions, and templates. It is more of an intermediate level language that can be used lower level or higher level, depending on the application. It is also known for being used in low latency applications. Objective-C Around the same time as C++ was being released to the public, Objective-C was created. If you took an educated guess from the name and said that it would be another extension of C, then you’d be right. This version was meant to be an object-oriented version of C (there’s a lot in a name, clearly). It is used, probably most famously, by Apple. If you are a Mac or iOS user, then your iPhone or Mac applications were most likely developed with Objective-C (until they recently moved over to Swift). Python We are going to take a quick jump ahead in time to the 90’s for this one. In 1991, the Python programming language was released, though it had been in development in the late 80’s. It is a dynamically-typed, object-oriented language that is often used for scripting and web applications. It is usually used with some of its frameworks like Django or Flask on the backend. It is one of the most popular programming languages in use today. Ruby In 1993, Ruby was released. Today, you probably heard of Ruby on Rails, which primarily is used to create the backend of web applications using Ruby. Unlike the many languages derived from C, this language was influenced by older languages such as Perl and Lisp. This language was designed for productive and fun programming. This was done by making the language closer to human needs, rather than machine needs. Java Two years later in 1995, Java was developed. This is a high level language that is derived from C. It is famously known for its use in web applications and as the language to use to develop Android applications and Android OS. It used to be the most popular language a few years ago, but its popularity and usage has definitely decreased. PHP In the same year as Java was developed, PHP was born. It is an open source programming language developed for the purpose of creating dynamic websites. It is also used for server side web development. Its usage is definitely declining, but it is still in use today. JavaScript That same year (yup, ’95 was good year for programming, not so much for fans of Full House), JavaScript was brought to the world. Its purpose was to be a high level language that helped with the functionality of a web page. Today, it is sometimes used as a scripting language, as well as being used on the backend of applications with the release of Node.js. It is one of the most popular and widely used programming languages today. Conclusion That was our brief history lesson on some in use programming languages. Even though some of them are 20, 30, even over 40 years old, they are being used by thousands of developers daily. They all have a variety of uses, from lower level to higher level, from web applications to mobile applications. Do you feel there is a need for newer languages, or are you happy with what we have? If you have any favorites, let us know which one and why! About the author Antonio Cucciniello is a Software Engineer with a background in C, C++ and JavaScript (Node.Js) from New Jersey.   His most recent project called Edit Docs is an Amazon Echo skill that allows users to edit Google Drive files using your voice.  He loves building cool things with software, reading books on self-help and improvement, finance, and entrepreneurship. Follow him on twitter @antocucciniello, and follow him on GitHub here: https://github.com/acucciniello

I read a post from a Linkedin connection about a week ago. It read: “The first step in becoming a data scientist: forget about Windows.” Even if you’re not a programmer, that's pretty controversial. The first nerdy thought I had was, that’s not true. The first step to Data Science is not choosing an OS, it’s statistics! Anyway, I kept wondering what’s wrong with doing data science on Windows, exactly. Why is the legacy product (Windows), created by one of the leaders in Data Science and Artificial Intelligence, not suitable to support the very thing it is driving? As a publishing professional and having worked with a lot of authors, one of the main issues I’ve faced while collaborating with them is the compatibility of platforms, especially when it comes to sharing documents, working with code, etc. At least 80 percent of the authors I’ve worked with have been using something other than Windows. They are extremely particular about the platform they’re working on, and have usually chosen Linux. I don’t know if they consider it a punishable offence, but I’ve been using Windows since I was 12, even though I have played around with Macs and machines running Linux/Unix. I’ve never been affectionately drawn towards those machines as much as my beloved laptop that is happily rolling on Windows 10 Pro. Why is data science on Windows is a bad idea? When Microsoft created Windows, its main idea was to make the platform as user friendly as possible, and it focused every ounce of energy on that and voila! They created one of the most simplest operating systems that one could ever use. Microsoft wanted to make computing easy for everyone - teachers, housewives, kids, business professionals. However, they did not consider catering to the developer community as much as its users. Now that’s not to say that you can’t really use a Windows machine to code. Of course, you can run Python or R programs. But you’re likely to face issues with compatibility and speed. If you’re choosing to use the command line, and something goes wrong, it’s a real PITA to debug on Windows. Also, if you’re doing cluster computing with other Linux/Macs, it’s better to have one of them yourself. Many would agree that Windows is more likely to suffer a BSoD (Blue Screen of Death) than a Mac or a Unix machine, messing up your algorithm that’s been running for a long time. [box type="note" align="" class="" width=""]Check out our most read post 15 useful Python libraries to make your Data science tasks easier. [/box] Is it all that bad? Well, not really. In fact, if you need to pump in a couple more gigs of RAM, you can’t think of doing that on a Mac. Although you might still encounter some weird stuff like those mentioned above, on a Windows PC, you can always Google up a workaround. Don’t beat yourself up if you own a PC. You can always set up a dual boot, running a Linux distribution parallely. You might want to check out Vagrant for this. Also, you’ll be surprised if you’re a Mac owner and you plan some heavy duty Deep Learning on a GPU, you can’t really run CUDA without messing things up. CUDA will only work well with NVIDIAs GPUs on a PC. In Joey Tribbiani's words “This is a moo point.” To me, data science is really OS agnostic. For instance, now with Docker, you don’t really have to worry much about which OS you’re running - so from that perspective, data science on Windows may work for you. Still feel for Windows? Well, there are obviously drawbacks. You’ll still keep living with the fear of isolation that Microsoft tries to create in the minds of customers. Moreover, you’ll be faced with “slowdom” if that’s a word, what with all the background processes eating away your computing power! You’ll be defying everything that modern computing is defined by - KISS, Open Source, Agile, etc. Another important thing you need to keep in mind is that when you’re working with so much data, you really don’t wanna get hacked! Last but not the least, if you’re intending to dabble with AI and Blockchain, your best bet is not going to be Windows. All said and done, if you’re a budding data scientist who’s looking to buy some new equipment, you might want to consider a few things before you invest in your machine. Think about what you’ll be working with, what tools you might want to use and if you want to play safe, it’s best to go with a Linux system. If you have the money and want to flaunt it, while still enjoying support from most tools, think about a Mac. And finally, if you’re brave and are not worried about having two OSes running on your system, go in for a Windows PC. So the next time someone decides to gift you a Windows PC, don’t politely decline right away. Grab it and swiftly install a Linux distro! Happy coding! :) *I will put an asterisk here, for the thoughts put in this article are completely my personal opinion and it might differ from person to person. Go ahead and share your thoughts in the comments section below.

What is React.js? React.js is one of the most talked about JavaScript web frameworks in years. Alongside Angular, and more recently Vue, React is a critical tool that has had a big impact on the way we build web applications. But it's hard to find a better description of React.js than the single sentence on the project's home page: A JavaScript library for building user interfaces. It's a library. For building user interfaces. This is perfect because, more often than not, this is all we want. The best part about this description is that it highlights React's simplicity. It's not a mega framework. It's not a full-stack solution that's going to handle everything from the database to real-time updates over web socket connections. We don't actually want most of these pre-packaged solutions, because in the end, they usually cause more problems than they solve. Facebook sure did listen to what we want. This is an extract from React and React Native by Adam Boduch. Learn more here. React.js is just the view. That's it. React.js is generally thought of as the view layer in an application. You might have used library like Handlebars, or jQuery in the past. Just as jQuery manipulates UI elements, or Handlebars templates are inserted onto the page, React components change what the user sees. The following diagram illustrates where React fits in our frontend code. This is literally all there is to React. We want to render this data to the UI, so we pass it to a React component which handles the job of getting the HTML into the page. You might be wondering what the big deal is. On the surface, React appears to be another rendering technology. But it's much more than that. It can make application development incredibly simple. That's why it's become so popular. React.js is simple React doesn't have many moving parts for us to learn about and understand. The advantage to having a small API to work with is that you can spend more time familiarizing yourself with it, experimenting with it, and so on. The opposite is true of large frameworks, where all your time is devoted to figuring out how everything works. The following diagram gives a rough idea of the APIs that we have to think about when programming with React. React is divided into two major APIs. First, there's the React DOM. This is the API that's used to perform the actual rendering on a web page. Second, there's the React component API. These are the parts of the page that are actually rendered by React DOM. Within a React component, we have the following areas to think about: Data: This is data that comes from somewhere (the component doesn't care where), and is rendered by the component. Lifecycle: These are methods that we implement that respond to changes in the lifecycle of the component. For example, the component is about to be rendered. Events: This is code that we write for responding to user interactions. JSX: This is the syntax of React components used to describe UI structures. Don't fixate on what these different areas of the React API represent just yet. The takeaway here is that React is simple. Just look at how little there is to figure out! This means that we don't have to spend a ton of time going through API details here. Instead, once you pick up on the basics, you can spend more time on nuanced React usage patterns. React has a declarative UI structure React newcomers have a hard time coming to grips with the idea that components mix markup in with their JavaScript. If you've looked at React examples and had the same adverse reaction, don't worry. Initially, we're all skeptical of this approach, and I think the reason is that we've been conditioned for decades by the separation of concerns principle. Now, whenever we see things mixed together, we automatically assume that this is bad and shouldn't happen. The syntax used by React components is called JSX (JavaScript XML). The idea is actually quite simple. A component renders content by returning some JSX. The JSX itself is usually HTML markup, mixed with custom tags for the React components. What's absolutely groundbreaking here is that we don't have to perform little micro-operations to change the content of a component. For example, think about using something like jQuery to build your application. You have a page with some content on it, and you want to add a class to a paragraph when a button is clicked. Performing these steps is easy enough, but the challenge is that there are steps to perform at all. This is called imperative programming, and it's problematic for UI development. While this example of changing the class of an element in response to an event is simple, real applications tend to involve more than 3 or 4 steps to make something happen. Read more: 5 reasons to learn React React components don't require executing steps in an imperative way to render content. This is why JSX is so central to React components. The XML-style syntax makes it easy to describe what the UI should look like. That is, what are the HTML elements that this component is going to render? This is called declarative programming, and is very well-suited for UI development. Time and data Another area that's difficult for React newcomers to grasp is the idea that JSX is like a static string, representing a chunk of rendered output. Are we just supposed to keep rendering this same view? This is where time and data come into play. React components rely on data being passed into them. This data represents the dynamic aspects of the UI. For example, a UI element that's rendered based on a Boolean value could change the next time the component is rendered. Here's an illustration of the idea. Each time the React component is rendered, it's like taking a snapshot of the JSX at that exact moment in time. As our application moves forward through time, we have an ordered collection of rendered user interface components. In addition to declaratively describing what a UI should be, re-rendering the same JSX content makes things much easier for developers. The challenge is making sure that React can handle the performance demands of this approach. Performance matters with React Using React to build user interfaces means that we can declare the structure of the UI with JSX. This is less error-prone than the imperative approach to assembling the UI piece by piece. However, the declarative approach does present us with one challenge—performance. For example, having a declarative UI structure is fine for the initial rendering, because there's nothing on the page yet. So the React renderer can look at the structure declared in JSX, and render it into the browser DOM. This is illustrated below. On the initial render, React components and their JSX are no different from other template libraries. For instance, Handlebars will render a template to HTML markup as a string, which is then inserted into the browser DOM. Where React is different from libraries like Handlebars is when data changes, and we need to re-render the component. Handlebars will just rebuild the entire HTML string, the same way it did on the initial render. Since this is problematic for performance, we often end up implementing imperative workarounds that manually update tiny bits of the DOM. What we end up with is a tangled mess of declarative templates, and imperative code to handle the dynamic aspects of the UI. We don't do this in React. This is what sets React apart from other view libraries. Components are declarative for the initial render, and they stay this way even as they're re-rendered. It's what React does under the hood that makes re-rendering declarative UI structures possible. React has something called the virtual DOM, which is used to keep a representation of the real DOM elements in memory. It does this so that each time we re-render a component, it can compare the new content, to the content that's already displayed on the page. Based on the difference, the virtual DOM can execute the imperative steps necessary to make the changes. So not only do we get to keep our declarative code when we need to update the UI, React will also make sure that it's done in a performant way. Here's what this process looks like: When you read about React, you'll often see words like diffing and patching. Diffing means comparing old content with new content to figure out what's changed. Patching means executing the necessary DOM operations to render the new content React.js has the right level of abstraction React.js doesn't have a great deal of abstraction, but the abstractions the framework does implement are crucial to its success. In the preceding section, you saw how JSX syntax translates to the low-level operations that we have no interest in maintaining. The more important way to look at how React translates our declarative UI components is the fact that we don't necessarily care what the render target is. The render target happens to be the browser DOM with React. But this is changing. We're only just starting to see this with React Native, but the possibilities are endless. I personally will not be surprised when React Toast becomes a thing, targeting toasters that can singe the rendered output of JSX on to bread. The abstraction level with React is at the right level, and it's in the right place. The following diagram gives you an idea of how React can target more than just the browser. From left to right, we have React Web (just plain React), React Native, React Desktop, and React Toast. As you can see, to target something new, the same pattern applies: Implement components specific to the target Implement a React renderer that can perform the platform-specific operations under the hood Profit This is obviously an oversimplification of what's actually implemented for any given React environment. But the details aren't so important to us. What's important is that we can use our React knowledge to focus on describing the structure of our user interface on any platform. Disclaimer: React Toast will probably never be a thing, unfortunately.