Part 4:Deep Learning Modeling
In this part of the book, we will lay the foundation with an introduction to the underlying theories of deep learning, and then transition to hands-on exploration of fully connected neural networks. We will then learn about more advanced techniques including convolutional neural networks, transformers, and graph neural networks. Concluding this part, we will spotlight the cutting-edge advancements in machine learning, with a keen focus on generative modeling and an introduction to reinforcement and self-supervised learning. Throughout these chapters, practical examples are provided using Python and PyTorch, ensuring that we gain both theoretical knowledge as well as hands-on experience.
This part has the following chapters:
- Chapter 12, Going Beyond ML Debugging with Deep Learning
- Chapter 13, Advanced Deep Learning Techniques
- Chapter 14, Introduction to Recent Advancements in Machine Learning
...
