Deep learning essentials : your hands-on guide to the fundamentals of deep learning and neural network modeling / Wei Di, Anurag Bhardwaj, Jianing Wei.

Format:

Book

Author/Creator:

Di, Wei, author.

Bhardwaj, Anurag, author.

Wei, Jianing, author.

Language:

English

Subjects (All):

Artificial intelligence.

Neural networks (Computer science).

Physical Description:

1 online resource (284 pages)

Edition:

1st edition

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2018.

System Details:

text file

Summary:

Get to grips with the essentials of deep learning by leveraging the power of Python About This Book Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Who This Book Is For Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python. What You Will Learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU In Detail Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while traini...

Contents:

Cover

Title Page

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: Why Deep Learning?

What is AI and deep learning?

The history and rise of deep learning

Why deep learning?

Advantages over traditional shallow methods

Impact of deep learning

The motivation of deep architecture

The neural viewpoint

The representation viewpoint

Distributed feature representation

Hierarchical feature representation

Applications

Lucrative applications

Success stories

Deep learning for business

Future potential and challenges

Summary

Chapter 2: Getting Yourself Ready for Deep Learning

Basics of linear algebra

Data representation

Data operations

Matrix properties

Deep learning with GPU

Deep learning hardware guide

CPU cores

RAM size

Hard drive

Cooling systems

Deep learning software frameworks

TensorFlow - a deep learning library

Caffe

MXNet

Torch

Theano

Microsoft Cognitive Toolkit

Keras

Framework comparison

Setting up deep learning on AWS

Setup from scratch

Setup using Docker

Chapter 3: Getting Started with Neural Networks

Multilayer perceptrons

The input layer

The output layer

Hidden layers

Activation functions

Sigmoid or logistic function

Tanh or hyperbolic tangent function

ReLU

Leaky ReLU and maxout

Softmax

Choosing the right activation function

How a network learns

Weight initialization

Forward propagation

Backpropagation

Calculating errors

Updating the network

Automatic differentiation

Vanishing and exploding gradients

Optimization algorithms

Regularization

Deep learning models

Convolutional Neural Networks

Convolution

Pooling/subsampling

Fully connected layer

Overall.

Restricted Boltzmann Machines

Energy function

Encoding and decoding

Contrastive divergence (CD-k)

Stacked/continuous RBM

RBM versus Boltzmann Machines

Recurrent neural networks (RNN/LSTM)

Cells in RNN and unrolling

Backpropagation through time

Vanishing gradient and LTSM

Cells and gates in LTSM

Step 1 - The forget gate

Step 2 - Updating memory/cell state

Step 3 - The output gate

Practical examples

TensorFlow setup and key concepts

Handwritten digits recognition

Chapter 4: Deep Learning in Computer Vision

Origins of CNNs

Data transformations

Input preprocessing

Data augmentation

Network layers

Convolution layer

Pooling or subsampling layer

Fully connected or dense layer

Network initialization

Loss functions

Model visualization

Handwritten digit classification example

Fine-tuning CNNs

Popular CNN architectures

AlexNet

Visual Geometry Group

GoogLeNet

ResNet

Chapter 5: NLP - Vector Representation

Traditional NLP

Bag of words

Weighting the terms tf-idf

Deep learning NLP

Motivation and distributed representation

Word embeddings

Idea of word embeddings

Advantages of distributed representation

Problems of distributed representation

Commonly used pre-trained word embeddings

Word2Vec

Basic idea of Word2Vec

The word windows

Generating training data

Negative sampling

Hierarchical softmax

Other hyperparameters

Skip-Gram model

The hidden layer

The loss function

Continuous Bag-of-Words model

Training a Word2Vec using TensorFlow

Using existing pre-trained Word2Vec embeddings

Word2Vec from Google News

Using the pre-trained Word2Vec embeddings

Understanding GloVe

FastText.

Applications

Example use cases

Fine-tuning

Chapter 6: Advanced Natural Language Processing

Deep learning for text

Limitations of neural networks

Recurrent neural networks

RNN architectures

Basic RNN model

Training RNN is tough

Long short-term memory network

LSTM implementation with tensorflow

Language modeling

Sequence tagging

Machine translation

Seq2Seq inference

Chatbots

Chapter 7: Multimodality

What is multimodality learning?

Challenges of multimodality learning

Representation

Translation

Alignment

Fusion

Co-learning

Image captioning

Show and tell

Encoder

Decoder

Training

Testing/inference

Beam Search

Other types of approaches

Datasets

Evaluation

BLEU

ROUGE

METEOR

CIDEr

SPICE

Rank position

Attention models

Attention in NLP

Attention in computer vision

The difference between hard attention and soft attention

Visual question answering

Multi-source based self-driving

Chapter 8: Deep Reinforcement Learning

What is reinforcement learning (RL)?

Problem setup

Value learning-based algorithms

Policy search-based algorithms

Actor-critic-based algorithms

Deep reinforcement learning

Deep Q-network (DQN)

Experience replay

Target network

Reward clipping

Double-DQN

Prioritized experience delay

Dueling DQN

Implementing reinforcement learning

Simple reinforcement learning example

Reinforcement learning with Q-learning example

Chapter 9: Deep Learning Hacks

Massaging your data

Data cleaning

Data normalization

Tricks in training

All-zero

Random initialization

ReLU initialization

Xavier initialization

Optimization

Learning rate

Mini-batch.

Clip gradients

Choosing the loss function

Multi-class classification

Multi-class multi-label classification

Regression

Others

Preventing overfitting

Batch normalization

Dropout

Early stopping

When to use fine-tuning

When not to use fine-tuning

Tricks and techniques

Model compression

Chapter 10: Deep Learning Trends

Recent models for deep learning

Generative Adversarial Networks

Capsule networks

Novel applications

Genomics

Predictive medicine

Clinical imaging

Lip reading

Visual reasoning

Code synthesis

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Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (EBC, viewed March 6, 2018).

OCLC:

1022788253