**Autor: Sudharsan Ravichandiran**

Broj strana: 512

ISBN broj: 9781789344158

Izdavač:
PACKT PUBLISHING

Godina izdanja: 2020.

Pregleda (30 dana / ukupno): **97 / 97**

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- 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

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.

- 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

**Table of contents**

**1 Section 1: Getting Started with Deep Learning ****2 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 **3 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 **4 Section 2: Fundamental Deep Learning Algorithms ****5 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 **6 Generating Song Lyrics Using RNN **

Introducing RNNs

Generating song lyrics using RNNs

Different types of RNN architectures

Summary

Questions

Further reading **7 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 **8 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 **9 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 **10 Section 3: Advanced Deep Learning Algorithms ****11 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 **12 Learning More about GANs **

Conditional GANs

Understanding InfoGAN

Translating images using a CycleGAN

StackGAN

Summary

Questions

Further reading **13 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 **14 Exploring Few-Shot Learning Algorithms **

What is few-shot learning?

Siamese networks

Architecture of siamese networks

Prototypical networks

Relation networks

Matching networks

Summary

Questions

Further reading **15 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 **16 Other Books You May Enjoy **

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