Hands-On Deep Learning Algorithms with Python

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.

Type
ebook
Category
Data
publication date
2019-07-25
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

no of pages
512
duration
1024
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
approach
This book is a perfect beginner’s guide to understanding different deep learning algorithms, from basic to advanced.
audience
If you are a machine learning engineer, data scientist, AI developer, or anyone looking to delve into 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.
meta description
Understand basic-to-advanced deep learning algorithms, the mathematical principles behind them, and their practical applications
short description
This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such as TensorFlow.
subtitle
Master deep learning algorithms with extensive math by implementing them using TensorFlow
keywords
Deep learning, python, tensorflow, keras, convolutional neural network, cnn, recurrent neural network, RNN, Artificial Intelligence, artificial neural network, deep belief networks, generative model, GAN, generative adversarial network, Machine learning
Product ISBN
9781789344158