Neural Network Programming with Tensorflow

Neural Networks and their implementation decoded with TensorFlow
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Neural Network Programming with Tensorflow

Manpreet Singh Ghotra, Rajdeep Dua

Neural Networks and their implementation decoded with TensorFlow
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Book Details

ISBN 139781788390392
Paperback274 pages

Book Description

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.

Table of Contents

Chapter 1: Maths for Neural Networks
Understanding linear algebra
Calculus
Optimization
Summary
Chapter 2: Deep Feedforward Networks
Defining feedforward networks
Understanding backpropagation
Implementing feedforward networks with TensorFlow
Analyzing the Iris dataset
Implementing feedforward networks with images
Summary
Chapter 3: Optimization for Neural Networks
What is optimization?
Types of optimizers
Gradient descent
Which optimizer to choose
Summary
Chapter 4: Convolutional Neural Networks
An overview and the intuition of CNN
Convolution operations
Pooling
Image classification with convolutional networks
Summary
Chapter 5: Recurrent Neural Networks
Introduction to RNNs
Introduction to long short term memory networks
Sentiment analysis
Summary
Chapter 6: Generative Models
Generative models
GANs
Summary
Chapter 7: 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
Chapter 8: Autoencoders
Autoencoder algorithms
Under-complete autoencoders
Dataset
Basic autoencoders
Additive Gaussian Noise autoencoder
Sparse autoencoder
Summary
Chapter 9: 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
Chapter 10: Getting started with TensorFlow
Environment setup
TensorFlow comparison with Numpy
Computational graph
Auto differentiation
TensorBoard

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.

Authors

Table of Contents

Chapter 1: Maths for Neural Networks
Understanding linear algebra
Calculus
Optimization
Summary
Chapter 2: Deep Feedforward Networks
Defining feedforward networks
Understanding backpropagation
Implementing feedforward networks with TensorFlow
Analyzing the Iris dataset
Implementing feedforward networks with images
Summary
Chapter 3: Optimization for Neural Networks
What is optimization?
Types of optimizers
Gradient descent
Which optimizer to choose
Summary
Chapter 4: Convolutional Neural Networks
An overview and the intuition of CNN
Convolution operations
Pooling
Image classification with convolutional networks
Summary
Chapter 5: Recurrent Neural Networks
Introduction to RNNs
Introduction to long short term memory networks
Sentiment analysis
Summary
Chapter 6: Generative Models
Generative models
GANs
Summary
Chapter 7: 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
Chapter 8: Autoencoders
Autoencoder algorithms
Under-complete autoencoders
Dataset
Basic autoencoders
Additive Gaussian Noise autoencoder
Sparse autoencoder
Summary
Chapter 9: 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
Chapter 10: Getting started with TensorFlow
Environment setup
TensorFlow comparison with Numpy
Computational graph
Auto differentiation
TensorBoard

Book Details

ISBN 139781788390392
Paperback274 pages
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