Hands-On Deep Learning Algorithms with Python: Master deep learning algorithms with extensive math by implementing them using TensorFlow

Hands-On Deep Learning Algorithms with Python: Master deep learning algorithms with extensive math by implementing them using TensorFlow
Hands-On Deep Learning Algorithms with Python: Master deep learning algorithms with extensive math by implementing them using TensorFlow by Sudharsan Ravichandiran
English | 2019 | ISBN: 1789344158 | 512 Pages | EPUB | 388 MB

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