Neural network programming with tensorflow : unleash the power of tensorflow to train efficient neural networks / Manpreet Singh Ghotra, Rajdeep Dua.

Format:

Book

Author/Creator:

Ghotra, Manpreet Singh, author.

Dua, Rajdeep, author.

Language:

English

Subjects (All):

Algebras, Linear.

Neural networks (Computer science).

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Other Title:

Unleash the power of TensorFlow to train efficient neural networks

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Biography/History:

Ghotra Manpreet Singh: Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community. Dua Rajdeep: Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.

Summary:

Neural Networks and their implementation decoded with TensorFlow About This Book Develop a strong background in neural network programming from scratch, using the popular Tensorflow library. Use Tensorflow to implement different kinds of neural networks ? from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and more. A highly practical guide including real-world datasets and use-cases to simplify your understanding of neural networks and their implementation. Who This Book Is For This book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, book can be used as a generic resource to bridge the gap between the math and the implementation of deep learning. If you have some understanding of Tensorflow and Python and want to learn what happens at a level lower than the plain API syntax, this book is for you. What You Will Learn Learn Linear Algebra and mathematics behind neural network. Dive deep into Neural networks from the basic to advanced concepts like CNN, RNN Deep Belief Networks, Deep Feedforward Networks. Explore Optimization techniques for solving problems like Local minima, Global minima, Saddle points Learn through real world examples like Sentiment Analysis. Train different types of generative models and explore autoencoders. Explore TensorFlow as an example of deep learning implementation. In Detail If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that. You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to...

Contents:

Cover

Copyright

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Maths for Neural Networks

Understanding linear algebra

Environment setup

Setting up the Python environment in Pycharm

Linear algebra structures

Scalars, vectors, and matrices

Tensors

Operations

Vectors

Matrices

Matrix multiplication

Trace operator

Matrix transpose

Matrix diagonals

Identity matrix

Inverse matrix

Solving linear equations

Singular value decomposition

Eigenvalue decomposition

Principal Component Analysis

Calculus

Gradient

Hessian

Determinant

Optimization

Optimizers

Summary

Chapter 2: Deep Feedforward Networks

Defining feedforward networks

Understanding backpropagation

Implementing feedforward networks with TensorFlow

Analyzing the Iris dataset

Code execution

Implementing feedforward networks with images

Analyzing the effect of activation functions on the feedforward networks accuracy

Chapter 3: Optimization for Neural Networks

What is optimization?

Types of optimizers

Gradient descent

Different variants of gradient descent

Algorithms to optimize gradient descent

Which optimizer to choose

Optimization with an example

Chapter 4: Convolutional Neural Networks

An overview and the intuition of CNN

Single Conv Layer Computation

CNN in TensorFlow

Image loading in TensorFlow

Convolution operations

Convolution on an image

Strides

Pooling

Max pool

Example code

Average pool

Image classification with convolutional networks

Defining a tensor for input images and the first convolution layer

Input tensor

First convolution layer

Second convolution layer

Third convolution layer.

Flatten the layer

Fully connected layers

Defining cost and optimizer

Optimizer

First epoch

Plotting filters and their effects on an image

Chapter 5: Recurrent Neural Networks

Introduction to RNNs

RNN implementation

Computational graph

RNN implementation with TensorFlow

Introduction to long short term memory networks

Life cycle of LSTM

LSTM implementation

Sentiment analysis

Word embeddings

Sentiment analysis with an RNN

Chapter 6: Generative Models

Generative models

Discriminative versus generative models

Types of generative models

Autoencoders

GAN

Sequence models

GANs

GAN with an example

Types of GANs

Vanilla GAN

Conditional GAN

Info GAN

Wasserstein GAN

Coupled GAN

Chapter 7: Deep Belief Networking

Understanding deep belief networks

DBN implementation

Class initialization

RBM class

Pretraining the DBN

Model training

Predicting the label

Finding the accuracy of the model

DBN implementation for the MNIST dataset

Loading the dataset

Input parameters for a DBN with 256-Neuron RBM layers

Output for a DBN with 256-neuron RBN layers

Effect of the number of neurons in an RBM layer in a DBN

An RBM layer with 512 neurons

An&amp

#160

RBM layer with 128 neurons

Comparing the accuracy metrics

DBNs with two RBM layers

Classifying the NotMNIST dataset with a DBN

Chapter 8: Autoencoders

Autoencoder algorithms

Under-complete autoencoders

Dataset

Basic autoencoders

Autoencoder initialization

AutoEncoder class

Basic autoencoders with MNIST data

Basic autoencoder plot of weights

Basic autoencoder recreated images plot

Basic autoencoder full code listing.

Basic autoencoder summary

Additive Gaussian Noise autoencoder

Autoencoder class

Additive Gaussian Autoencoder with the MNIST dataset

Training the model

Plotting the weights

Plotting the reconstructed images

Additive Gaussian autoencoder full code listing

Comparing basic encoder costs with the Additive Gaussian Noise autoencoder

Additive Gaussian Noise autoencoder summary

Sparse autoencoder

KL divergence

KL divergence in TensorFlow

Cost of a sparse autoencoder based on KL Divergence

Complete code listing of&amp

the sparse autoencoder

Sparse autoencoder on MNIST data

Comparing the Sparse encoder with&amp

the Additive Gaussian Noise encoder

Chapter 9: Research in Neural Networks

Avoiding overfitting in neural networks

Problem statement

Solution

Results

Large-scale video processing with neural networks

Resolution improvements

Feature histogram baselines

Quantitative results

Named entity recognition using a twisted neural network

Example of a named entity recognition

Defining Twinet

Bidirectional RNNs

BRNN on TIMIT dataset

Appendix: Getting started with TensorFlow

TensorFlow comparison with Numpy

Graph

Session objects

Variables

Scope

Data input

Placeholders and feed dictionaries

Auto differentiation

TensorBoard

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (EBC, viewed December 12, 2017).

ISBN:

9781788397759

1788397754

OCLC:

1015687249