This chapter acts as a prelude to the entire book and the concepts within it. We will understand these concepts at a level high enough for us to appreciate what we will be building throughout the book.
We will start by getting our head around the general structure of Artificial Intelligence (AI) and its building blocks by comparing AI, machine learning, and deep learning, as these terms can be used interchangeably. Then, we will skim through the history, evolution, and principles behind Artificial Neural Networks (ANNs). Later, we will dive into the fundamental concepts and terms of ANNs and deep learning that will be used throughout the book. After that, we take a brief look at the TensorFlow Playground to reinforce our understanding of ANNs. Finally, we will finish off the chapter with thoughts on where to get a deeper theoretical reference for the high-level concepts of the AI and ANN principles covered in this chapter, which will be as follows:
- AI versus machine learning versus deep learning
- Evolution of AI
- The mechanics behind ANNs
- Biological neurons
- Working of artificial neurons
- Activation and cost functions
- Gradient descent, backpropagation, and softmax
- TensorFlow Playground
