Hands-On Deep Learning Algorithms with Python

Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications.

Key Features

• Get up-to-speed with building your own neural networks from scratch
• Gain insights into the mathematical principles behind deep learning algorithms
• Implement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlow

Book Description

Deep learning is one of the most popular domains in the AI space, allowing 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 behind it, 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 RNNs and 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. Then you learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Afterward, you will explore various 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.

What you will learn

• Implement basic-to-advanced deep learning algorithms
• Master the mathematics behind deep learning algorithms
• Become familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and Nadam
• Implement recurrent networks, such as RNN, LSTM, GRU, and seq2seq models
• Understand how machines interpret images using CNN and capsule networks
• Implement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGAN
• Explore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAE

Who this book is for

If you are a machine learning engineer, data scientist, AI developer, or simply want to focus on neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming, will also find the book very helpful.