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

What is deep learning?

Deep learning is just a modern name for artificial neural networks with many layers. What is deep in deep learning though? It is basically due to the structure of the artificial neural network (ANN). ANN consists of some n number of layers to perform any computation. We can build an ANN with several layers where each layer is responsible for learning the intricate patterns in the data. Due to the computational advancements, we can build a network even with 100s or 1000s of layers deep. Since the ANN uses deep layers to perform learning we call it as deep learning and when ANN uses deep layers to learn we call it as a deep network. We have learned that deep learning is a subset of machine learning. How does deep learning differ from machine learning? What makes deep learning so special and popular?

The success of machine learning lies in the right set of...