Introduction

In the previous chapter, we learned how to implement basic mathematical concepts such as quadratic equations, linear algebra, and matrix multiplication in TensorFlow. Now that we have learned the basics, let's dive into Artificial Neural Networks (ANNs), which are central to artificial intelligence and deep learning.

Deep learning is a subset of machine learning. In supervised learning, we often use traditional machine learning techniques, such as support vector machines or tree-based models, where features are explicitly engineered by humans. However, in deep learning, the model explores and identifies the important features of a labeled dataset without human intervention. ANNs, inspired by biological neurons, have a layered representation, which helps them learn labels incrementally—from the minute details to the complex ones. Consider the example of image recognition: in a given image, an ANN would just as easily identify basic details such as light and...