Deep learning is one of the most popular domains in the artificial intelligence (AI) space, which 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’ll gain insights into each algorithm, the mathematical principles behind it, and how to implement them in the best possible manner.

The book starts by explaining how you can build your own neural network, followed by introducing you to TensorFlow; the powerful Python-based library for machine learning and deep learning. Next, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, Nadam, and more. The book will then provide you with insights into the working of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) and how to generate song lyrics with RNN. Next, you will master the math for convolutional and Capsule networks, widely used for image recognition tasks. Towards the concluding chapters, you will learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Then you will explore various GANs such as InfoGAN and LSGAN and also autoencoders such as contractive autoencoders, VAE, and so on.

By the end of this book, you will be equipped with the skills needed to implement deep learning in your own projects.