Book Image

Hands-On Deep Learning Algorithms with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Deep Learning Algorithms with Python

By: Sudharsan Ravichandiran

Overview of this book

Deep learning is one of the most popular domains in the AI space that allows you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles involved, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into recurrent neural networks (RNNs) and LSTM and how to generate song lyrics with RNN. Next, you will master the math necessary to work with convolutional and capsule networks, widely used for image recognition tasks. You will also learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Finally, you will explore GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Getting Started with Deep Learning
4
Section 2: Fundamental Deep Learning Algorithms
10
Section 3: Advanced Deep Learning Algorithms

Summary

We started the chapter by learning about conditional GANs and how they can be used to generate our image of interest.

Later, we learned about InfoGANs, where the code c is inferred automatically based on the generated output, unlike CGAN, where we explicitly specify c. To infer c, we need to find the posterior, , which we don't have access to. So, we use an auxiliary distribution. We used mutual information to maximize the mutual information, , to maximize our knowledge about c given the generator output.

Then, we learned about CycleGANs, which map the data from one domain to another domain. We tried to learn the mapping from the distribution of images from photos domain to the distribution of images in paintings domain. Finally, we understood how StackGANs generate photorealistic images from a text description.

In the next chapter, we will learn about autoencoders...