Book description
Understand basictoadvanced 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 that allows you to develop multilayered 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 Pythonbased 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, skipgram, and PVDM. 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.
What you will learn
 Implement basictoadvanced 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 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.
Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github.com/PacktPublishing/HandsOnDeepLearningAlgorithmswithPython. If you require support please email: customercare@packt.com
Table of contents
 Title Page
 Copyright and Credits
 Dedication
 About Packt
 Contributors
 Preface
 Section 1: Getting Started with Deep Learning
 Introduction to Deep Learning

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
 Generating Song Lyrics Using RNN

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

Learning Text Representations
 Understanding the word2vec model
 Building the word2vec model using gensim
 Visualizing word embeddings in TensorBoard
 Doc2vec
 Understanding skipthoughts algorithm
 Quickthoughts 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
 Reconstructing Inputs Using Autoencoders
 Exploring FewShot Learning Algorithms

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 FewShot Learning Algorithms
 Other Books You May Enjoy
Product information
 Title: HandsOn Deep Learning Algorithms with Python
 Author(s):
 Release date: July 2019
 Publisher(s): Packt Publishing
 ISBN: 9781789344158
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