In a simpler sense, artificial intelligence is all about giving machines the ability to perform intelligently. For example, many of us can play chess. Essentially, we do this first by learning the fundamentals of playing the game and then we engage ourselves in actually playing the game with others. But can machines do this? Can machines learn on their own and play the game of chess with us? 

AI attempts to make this possible by giving us the power to synthesize what we call intelligence in terms of some rules and instill it into machines. Machines as mentioned here can be anything that can compute. For example, it could be software or a robot. 

There are actually several types of AI. The popular ones are the following:

• Fuzzy systems
• Expert systems
• ML systems

The final type sounds the most familiar here. We will...

Without taking any mathematical notations or too many theoretical details, let's try to approach the term Machine Learning (ML) from an intuitive perspective. For doing this, we will have to take a look at how we actually learn. Do you recollect, at school, when we were taught to identify the parts of speech in a sentence? We were presented with a set of rules to identify the part of the speeches in a sentence. We were given many examples and our teachers in the first place used to identify the parts of speeches in sentences for us to train us effectively so that we could use this learning experience to identify the parts of speeches in sentences that were not taught to us. Moreover, this learning process is fundamentally applicable to anything that we learn.  

What if we could similarly train the machines? What if we...

Now comes the most exciting part and probably the hottest technical term of this century. Reality apart, we now understand the learning to some extent, so let's get to the first part of the term deep learning—deep. 

DL is a type of machine learning but it is purely based on neural networks. We will take a look at neural networks too but in the next chapter. The basic objective of any machine learning system is to learn useful representations of the data given to it. But what makes DL different? It turns out that DL systems treat data as a representation of layers. For example, an image can be treated as a representation of layers of varying properties such as edges, contours, orientation, texture, and gradients. The following diagram from the book, Deep Learning with Python, by François Chollet captures this idea nicely:

In...

To make sure that our basics are clear regarding the distinction between AI, ML, and DL, let's refer to the following diagram, which elegantly captures the relationship between these three big names:

The diagram is quite self-explanatory and it has been referred to in many books in the field of DL. Let's try drawing an interesting conclusion from this diagram.

The statement may appear slightly confusing at first glance, but if we got our basics right, then this captures the distinction between AI, ML, and DL beautifully. We will proceed toward revisiting some of the necessary ML terminologies and concepts that will be required in the latter parts of this book. 

We have already seen what is meant by ML. In this section, we will focus on several terminologies such as supervised learning and unsupervised learning, and we will be taking a look at the steps involved in a standard ML workflow. But you may ask: why ML? We are supposed to learn about the applications of DL in this book. We just learned that DL is a type of ML only. Therefore, a quick overview of the basic ML-related concepts will certainly help. Let's start with several types of ML and how they differ from each other. 

ML encompasses a multitude of algorithms and topics. While every such algorithm that makes up an ML model is nothing but a mathematical computation on...

Any project starts with a problem in mind and ML projects are no exception. Before starting an ML project, it is very important to have a clear understanding of the problem that you are trying to solve using ML. Therefore, problem formulation and mapping with respect to the standard ML workflow serve as good starting points in an ML project. But what is meant by an ML workflow? This section is all about that. 

Designing ML systems and employing them to solve complex problems requires a set of skills other than just ML. It is good to know that ML requires knowledge of several things such as statistics, domain knowledge, software engineering, feature engineering, and basic high-school mathematics in varying proportions. To be able to design such systems, certain steps are fundamental to almost any ML workflow and each of these steps requires a certain...

If you have been a regular user of the World Wide Web since 2014, you'd agree to a visible rapid flurry of changes in websites. From solving ReCaptcha challenges of increasingly illegible writing to being automatically marked as human in the background, web development has been one of the forerunners in the display of the wealth of artificial intelligence that has been created over the last two decades.

Sir Tim Berners-Lee, attributed as the inventor of the internet, has put forward his views on a Semantic Web:

The growth spurt of AI saw several contenders running to make the most out of it. Over the last two decades, several individuals, start-ups, and even huge industrialists have sought to reap the benefits offered by the applications of AI. There are products in the market to whom artificial intelligence serves as the very heart of their business.

In this chapter, we briefly introduced many important concepts and terminologies that are vital to execute an ML project in general. These are going to be helpful throughout this book.

We started with what AI is and its three major types. We took a look at the factors that are responsible for the AI explosion that is happening around us. We then took a quick tour of several components of ML and how they contribute to an ML project. We saw what DL is and how AI, ML, and DL are connected.

Toward the very end of this chapter, we saw some examples where AI is being merged with web technologies to make intelligent applications that promise to solve complex problems. Behind almost all of the AI-enabled applications sits DL.

In the next chapters, we are going to leverage DL to make smart web applications.