Book Image

Neural Network Programming with TensorFlow

By : Manpreet Singh Ghotra, Rajdeep Dua
Book Image

Neural Network Programming with TensorFlow

By: Manpreet Singh Ghotra, Rajdeep Dua

Overview of this book

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 get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.

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Table of Contents (17 chapters)
Title Page
Title Page
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Maths for Neural Networks
Maths for Neural Networks
Understanding linear algebra
Calculus
Optimization
Summary
2
Deep Feedforward Networks
Deep Feedforward Networks
Defining feedforward networks
Understanding backpropagation
Implementing feedforward networks with TensorFlow
Analyzing the Iris dataset
Implementing feedforward networks with images
Summary
3
Optimization for Neural Networks
Optimization for Neural Networks
What is optimization?
Types of optimizers
Gradient descent
Which optimizer to choose
Summary
4
Convolutional Neural Networks
Convolutional Neural Networks
An overview and the intuition of CNN
Convolution operations
Pooling
Image classification with convolutional networks
Summary
5
Recurrent Neural Networks
Recurrent Neural Networks
Introduction to RNNs
Introduction to long short term memory networks
Sentiment analysis
Summary
6
Generative Models
Generative Models
Generative models
GANs
Summary
7
Deep Belief Networking
Deep Belief Networking
Understanding deep belief networks
Model training
Predicting the label
Finding the accuracy of the model
DBN implementation for the MNIST dataset
Effect of the number of neurons in an RBM layer in a DBN
DBNs with two RBM layers
Classifying the NotMNIST dataset with a DBN
Summary
8
Autoencoders
Autoencoders
Autoencoder algorithms
Under-complete autoencoders
Dataset
Basic autoencoders
Additive Gaussian Noise autoencoder
Sparse autoencoder
Summary
9
Research in Neural Networks
Research in Neural Networks
Avoiding overfitting in neural networks
Large-scale video processing with neural networks
Named entity recognition using a twisted neural network
Bidirectional RNNs
Summary
10
Getting started with TensorFlow
Getting started with TensorFlow
Environment setup
TensorFlow comparison with Numpy
Computational graph
Auto differentiation
TensorBoard

TensorFlow comparison with Numpy

TensorFlow and Numpy are both N-dimensional array libraries. TensorFlow additionally allows us to create tensor functions and compute derivatives. TensorFlow has become one of the major libraries used for deep learning as it is incredibly efficient and can run on GPUs.

The following program describes how TensorFlow and numpy can be used to perform similar operations such as creating tensors of a (3,3) shape:

import TensorFlow  as tf
import numpy as np

tf.InteractiveSession()

# TensorFlow  operations
a = tf.zeros((3,3))
b = tf.ones((3,3))

print(tf.reduce_sum(b, reduction_indices=1).eval())
print(a.get_shape())

# numpy operations
a = np.zeros((3, 3))
b = np.ones((3, 3))
print(np.sum(b, axis=1))
print(a.shape)

The output of the preceding code is as follows:

[ 3.  3.  3.]
(3, 3)
[ 3.  3.  3.]
(3, 3)
Previous Section
End of Section 3
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