In the first chapter, we had a very close look at deep learning and how it is related to machine learning and artificial intelligence. In this chapter, we are going to delve deeper into this topic. We will start off by learning about what sits at the heart of deep learning—namely, neural networks and their fundamental components, such as neurons, activation units, backpropagation, and so on.
Note that this chapter is not going to be too math heavy, but at the same time, we are not going to cut short the most important formulas that are fundamental to the world of neural networks. For a more math-heavy study of the subject, readers are encouraged to read the book Deep Learning (deeplearningbook.org) by Goodfellow et al.
The following is an overview of what we are going to cover in this chapter:
- A whirlwind tour of neural networks and their related concepts
- Deep learning versus shallow learning
- Different types of neural networks
- Setting up a deep-learning-based cloud environment
- Exploring Jupyter Notebooks