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

Sudharsan Ravichandiran

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4.1 (13 Ratings)

Paperback Jul 2019 512 pages 1st Edition

Sudharsan Ravichandiran

Free Trial

4.1 (13 Ratings)

Paperback Jul 2019 512 pages 1st Edition

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Key benefits

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

Description

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.

Who is this book for?

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.

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

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Product Details

Publication date : Jul 25, 2019

Length: 512 pages

Edition : 1st

Language : English

ISBN-13 : 9781789344158

Category :

Data

Languages :

Python

Concepts :

Deep Learning

Tools :

TensorFlow

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16 Chapters

Section 1: Getting Started with Deep Learning

Introduction to Deep Learning

What is deep learning?

Biological and artificial neurons

ANN and its layers

Exploring activation functions

Forward propagation in ANN

How does ANN learn?

Debugging gradient descent with gradient checking

Putting it all together

Summary

Questions

Further reading

Getting to Know TensorFlow

What is TensorFlow?

Understanding computational graphs and sessions

Variables, constants, and placeholders

Introducing TensorBoard

Handwritten digit classification using TensorFlow

Introducing eager execution

Math operations in TensorFlow

TensorFlow 2.0 and Keras

Should we use Keras or TensorFlow?

Summary

Questions

Further reading

Section 2: Fundamental Deep Learning Algorithms

Gradient Descent and Its Variants

Demystifying gradient descent

Gradient descent versus stochastic gradient descent

Momentum-based gradient descent

Adaptive methods of gradient descent

Summary

Questions

Further reading

Generating Song Lyrics Using RNN

Introducing RNNs

Generating song lyrics using RNNs

Different types of RNN architectures

Summary

Questions

Further reading

Improvements to the RNN

LSTM to the rescue

Gated recurrent units

Bidirectional RNN

Going deep with deep RNN

Language translation using the seq2seq model

Summary

Questions

Further reading

Demystifying Convolutional Networks

What are CNNs?

The architecture of CNNs

The math behind CNNs

Implementing a CNN in TensorFlow

CNN architectures

Capsule networks

Building Capsule networks in TensorFlow

Summary

Questions

Further reading

Learning Text Representations

Understanding the word2vec model

Building the word2vec model using gensim

Visualizing word embeddings in TensorBoard

Doc2vec

Understanding skip-thoughts algorithm

Quick-thoughts for sentence embeddings

Summary

Questions

Further reading

Section 3: Advanced Deep Learning Algorithms

Generating Images Using GANs

Differences between discriminative and generative models

Say hello to GANs!

DCGAN – Adding convolution to a GAN

Least squares GAN

GANs with Wasserstein distance

Summary

Questions

Further reading

Learning More about GANs

Conditional GANs

Understanding InfoGAN

Translating images using a CycleGAN

Reconstructing Inputs Using Autoencoders

What is an autoencoder?

Autoencoders with convolutions

Exploring denoising autoencoders

Understanding sparse autoencoders

Learning to use contractive autoencoders

Dissecting variational autoencoders

Summary

Questions

Further reading

Exploring Few-Shot Learning Algorithms

What is few-shot learning?

Siamese networks

Architecture of siamese networks

Prototypical networks

Assessments

Chapter 1 - Introduction to Deep Learning

Chapter 2 - Getting to Know TensorFlow

Chapter 3 - Gradient Descent and Its Variants

Chapter 4 - Generating Song Lyrics Using an RNN

Chapter 5 - Improvements to the RNN

Chapter 6 - Demystifying Convolutional Networks

Chapter 7 - Learning Text Representations

Chapter 8 - Generating Images Using GANs

Chapter 9 - Learning More about GANs

Chapter 10 - Reconstructing Inputs Using Autoencoders

Chapter 11 - Exploring Few-Shot Learning Algorithms

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About the author

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from 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.

See other products by Sudharsan Ravichandiran

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

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