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

Momentum-based gradient descent

In this section, we will learn about two new variants of gradient descent, called momentum and Nesterov accelerated gradient.

Gradient descent with momentum

We have a problem with SGD and mini-batch gradient descent due to the oscillations in the parameter update. Take a look at the following plot, which shows how mini-batch gradient descent is attaining convergence. As you can see, there are oscillations in the gradient steps. The oscillations are shown by the dotted line. As you may notice, it is making a gradient step toward one direction, and then taking a different direction, and so on, until it reaches convergence:

This oscillation occurs because, since we update the parameters after...