Hands-On Deep Learning Algorithms with Python

More Information
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
About

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.

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
Page Count 512
Course Length 15 hours 21 minutes
ISBN 9781789344158
Date Of Publication 25 Jul 2019

Authors

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist, researcher, Artificial Intelligence enthusiast, and YouTuber (search for "Sudharsan reinforcement learning"). He completed his Bachelor's in Information Technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, including Natural Language Processing and computer vision. He is an open source contributor and loves answering questions on Stack Overflow. He also authored a best-seller, Hands-On Reinforcement Learning with Python, published by Packt Publishing.