Tech Guides - Uncategorized

Python is the fastest growing programming language on the planet. This year’s Stack Overflow survey produces clear evidence that it is growing at an impressive rate. And it’s not really that surprising - versatile, dynamic, and actually pretty easy to learn, it’s a language that is accessible and powerful enough to solve problems in a range of fields, from statistics to building APIs. But what does the future hold for Python? How will it evolve to meet the needs of its growing community of engineers and analysts? Read the insights from 3 Python experts on what the future might hold for the programming language, taken from Python Interviews, a book that features 20 conversations with leading figures from the Python community. In the future, Python will spawn other more specialized languages Steve Holden (@HoldenWeb), CTO of Global Stress Index and former chairman and director of The PSF: I'm not really sure where the language is going. You hear loose talk of Python 4. To my mind though, Python is now at the stage where it's complex enough. Python hasn't bloated in the same way that I think the Java environment has. At that maturity level, I think it's rather more likely that Python's ideas will spawn other, perhaps more specialized, languages aimed at particular areas of application. I see this as fundamentally healthy and I have no wish to make all programmers use Python for everything; language choices should be made on pragmatic grounds. I've never been much of a one for pushing for change. Enough smart people are thinking about that already. So mostly I lurk on Python-Dev and occasionally interject a view from the consumer side, when I think that things are becoming a little too esoteric. The needs of the Python community are going to influence where the language goes in future Carol Willing (@WillingCarol), former director of The Python Foundation, core developer of CPython, and Research Software Engineer at Project Jupyter. I think we're going to continue to see growth in the scientific programming part of Python. So things that support the performance of Python as a language and async stability are going to continue to evolve. Beyond that, I think that Python is a pretty powerful and solid language. Even if you stopped development today, Python is a darn good language. I think that the needs of the Python community are going to feed back into Python and influence where the language goes. It's great that we have more representation from different groups within the core development team. Smarter minds than mine could provide a better answer to your question. I'm sure that Guido has some things in mind for where he wants to see Python go. Mobile development has been an Achilles' heel for Python for a long time. I'm hoping that some of the BeeWare stuff is going to help with the cross-compilation. A better story in mobile is definitely needed. But you know, if there's a need then Python will get there. I think that the language is going to continue to move towards the stuff that's in Python 3. Some big code bases, like Instagram, have now transitioned from Python 2 to 3. While there is much Python 2.7 code still in production, great strides have been made by Instagram, as they shared in their PyCon 2017 keynote. There's more tooling around Python 3 and more testing tools, so it's less risky for companies to move some of their legacy code to Python 3, where it makes business sense to. It will vary by company, but at some point, business needs, such as security and maintainability, will start driving greater migration to Python 3. If you're starting a new project, then Python 3 is the best choice. New projects, especially when looking at microservices and AI, will further drive people to Python 3. Organizations that are building very large Python codebases are adopting type annotations to help new developers Barry Warsaw (@pumpichank), member of the Python Foundation team at LinkedIn, former project leader of GNU Mailman: In some ways it's hard to predict where Python is going. I've been involved in Python for 23 years, and there was no way I could have predicted in 1994 what the computing world was going to look like today. I look at phones, IoT (Internet of things) devices, and just the whole landscape of what computing looks like today, with the cloud and containers. It's just amazing to look around and see all of that stuff. So there's no real way to predict what Python is going to look like even five years from now, and certainly not ten or fifteen years from now. I do think Python's future is still very bright, but I think Python, and especially CPython, which is the implementation of Python in C, has challenges. Any language that's been around for that long is going to have some challenges. Python was invented to solve problems in the 90s and the computing world is different now and is going to become different still. I think the challenges for Python include things like performance and multi-core or multi-threading applications. There are definitely people who are working on that stuff and other implementations of Python may spring up like PyPy, Jython, or IronPython. Aside from the challenges that the various implementations have, one thing that Python has as a language, and I think this is its real strength, is that it scales along with the human scale. For example, you can have one person write up some scripts on their laptop to solve a particular problem that they have. Python's great for that. Python also scales to, let's say, a small open source project with maybe 10 or 15 people contributing. Python scales to hundreds of people working on a fairly large project, or thousands of people working on massive software projects. Another amazing strength of Python as a language is that new developers can come in and learn it easily and be productive very quickly. They can pull down a completely new Python source code for a project that they've never seen before and dive in and learn it very easily and quickly. There are some challenges as Python scales on the human scale, but I feel like those are being solved by things like the type annotations, for example. On very large Python projects, where you have a mix of junior and senior developers, it can be a lot of effort for junior developers to understand how to use an existing library or application, because they're coming from a more statically-typed language. So a lot of organizations that are building very large Python codebases are adopting type annotations, maybe not so much to help with the performance of the applications, but to help with the onboarding of new developers. I think that's going a long way in helping Python to continue to scale on a human scale. To me, the language's scaling capacity and the welcoming nature of the Python community are the two things that make Python still compelling even after 23 years, and will continue to make Python compelling in the future. I think if we address some of those technical limitations, which are completely doable, then we're really setting Python up for another 20 years of success and growth.

Of late, Deep Learning has been one of the working forces behind most technological breakthroughs happening around the globe. Whether it is easy machine translation, automatic recognition and sorting of images, smartphone interaction, automated medicine and healthcare, deep learning is the power source for all. Neural networks, the building blocks of deep learning models, are now set on the path to achieve complete human brain imitation.  But, to achieve this, it faces a roadblock, the ability to perform sequential task learning without forgetting. This particular shortcoming is known as catastrophic forgetting. Humans too have a tendency of forgetting old information at a gradual rate. However, with neural networks this phenomenon occurs at a catastrophic rate and hence the name. In order to enable continual learning in neural networks, there are several powerful architectures and algorithms. Few of them are discussed in the article below: Long Short-Term Memory Networks Long Short-Term Memory network is a type of Recurrent Neural Network, generally used to solve the problem of vanishing gradient. It consists of an explicit memory unit called a cell, embedded into the network. As the name implies, LSTMs can remember information for longer duration. LSTM follows RNN architecture but unlike RNN they have 4 neural network layers. The cell runs straight down the entire architecture to store values. These stored values remain untouched as further learning happens. It can add new information to the cell state or eliminate old ones, regulated by three gates. These gates work on 1s(pass everything) and 0s(pass nothing). Further, the gates are responsible for protection and control of the cell state. If this sounds complex, here’s a simple connotation—The gates are the decision-makers in LSTM. They decide what information to eliminate and what to store. Based on the gate filter of the cell state, LSTM generates the output. LSTM is being used as a fundamental component by top multinational firms (Google, Amazon, Microsoft) for applications such as speech recognition, smart assistant, or for feature enhancement. Elastic Weight Consolidation Algorithm Synaptic consolidation is the human brain’s approach for long term learning. Elastic Weight consolidation algorithm has taken inspiration from this mechanism to solve the issue of catastrophic interference. The neural network, like the brain, is made up of several connections among the neurons. The EWC evaluates how important a task is to a connection. By evaluation we mean, assigning weights to a connection. These weights are decided based on the importance of the older tasks. In an EWC, the weight attached to each connection in a new task is linked to the old value by an elastic spring. The stiffness of the spring is in relation to the connection’s importance, hence the name, Elastic Weight Consolidation. In the play of weights and connections, EWC algorithm helps in making a neural network learn new tasks without overriding information of the prior task, reducing significant amount of computational cost. The EWC algorithm was used in Atari games to learn multiple games sequentially. Using an EWC, the game agent was able to learn to play one game and then transfer what it had learnt to play a new game. It was also able to play multiple games successively. Differentiable Neural Computer DeepMind’s Differentiable neural computer (DNC) is a memory augmented neural network (MANN) which is a combination of neural networks and memory system. DNCs can essentially store complex data as computers do, all the while learning from examples like neural networks. They are not only used to parse complex data structures such as trees and graphs but also learn to form their own data structure. When a DNC was shown a graph data structure for example, the map of the London Underground, it learnt to write a description of the graph and answered questions on the graph. Surprisingly, a DNC can also answer questions about your family tree! The DNC has a controller, one may think of it as a computer processor. But, the controller is responsible for three simple tasks: taking an input reading to and fro memory producing an interpretable output Memory here is referred to places where a vector of information is stored. A controller can fidget with read/write operations on the memory. With every new information it can either: choose to write to a completely new, unused location write to a used location based on the information the controller is searching for not perform the write operation at all It can also decide to free locations no longer needed. As far as reading is concerned, the controller can read from multiple memory locations. Memory can also be searched basis multiple parameters such as the content or the temporal links. The information read, can be further produced in the form of answers in context to the questions asked. Simply put, memory enables the DNCs to make decisions about how they allocate, store, and retrieve memory to produce relevant and interpretable answers. Progressive Neural Networks The ability to transfer knowledge across domains has limited applicability in case of neural networks. Progressive neural networks act as training wheels towards developing continual learning systems. It functions at each layer of the network to incorporate prior knowledge and to decide whether to reuse old computations or learn new ones, making itself immune to catastrophic forgetting. Progressive networks essentially operate in the form of an adapter to make connections between columns. A column here is a group of layers i.e. the training given to a neural network for a particular task. When a neural network has to learn a new task, an extra column is added and the weights of the first column are frozen, eliminating catastrophic forgetting. Output of the layers of the original column becomes additional input to layer in the new column. As more tasks are added, simultaneously the columns increase in number. The adapter then has to deal with the dimensionality explosion that may happen due to increasing number of columns. A progressively enhanced neural network was successful in playing the Labyrinth 3D maze game. The neural network progressively learnt new mazes by using information it received from previous mazes. Conclusion The memory augmented neural networks have wider application in the field of robotic process automation, self-driving cars, natural language understanding, chatbots, next word predictions etc. Neural networks are also being utilized for time series prediction essentially for AR and VR technologies, video analytics and to study financial markets. With the advancements happening in the field of Continual learning, a deep learning neural network that emulates the human brain entirely is nigh.