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Deep Feedforward Networks

In the first chapter, you learned about the mathematics which drives the logic behind all kinds of neural networks. In this chapter, we are going to focus on the most fundamental neutral networks, which are called feedforward neural networks. We will also look at deep feedforward networks with multiple hidden layers to improve the accuracy of the model.

We will be covering the following topics:

Defining feedforward networks
Understanding backpropagation
Implementing feedforward networks in TensorFlow
Analyzing the Iris dataset
Creating feedforward networks for image classification

Key benefits

• 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.

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.

Who is this book 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.

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Anurag Thakur May 09, 2018

I went through first two chapters till now...In the beginning they have given some links to download the code..I think that's enough as book don't have anything more.

Amazon Verified review

Dimitri Shvorob Mar 13, 2018

Wishing to learn about TensorFlow, I decided to survey TF books available from Amazon, and pick one or two for further study. I excluded self-published offerings, and ended up with this longish list, dominated by Packt titles:"Machine Learning with TensorFlow" by Shukla, published by Manning in 2018-02, 272 pp, $43"Mastering TensorFlow 1.x" by Fandango, Packt, 2018-01, 474 pp, $35"Pro Deep Learning with TensorFlow" by Pattanayak, Apress, 2017-12, 398 pp, $37"TensorFlow 1.x Deep Learning Cookbook" by Gulli and Kapoor, Packt, 2017-12, 536 pp, $32"Neural Network Programming with TensorFlow" by Ghotra and Dua, Packt, 2017-11, 274 pp, $40"Predictive Analytics with TensorFlow" by Karim, Packt, 2017-11, 522 pp, $50"Machine Learning with TensorFlow 1.x" by Hua and Azeem, Packt, 2017-11, 304 pp, $39"Learning TensorFlow" by Hope and Resheff, O'Reilly, 2017-08, 242 pp, $25"Hands-On Deep Learning with TensorFlow" by Van Boxel, Packt, 2017-07, 174 pp, $35"Deep Learning with TensorFlow" by Zaccone, Karim, Menshawy, Packt, 2017-04, 320 pp, $50"TensorFlow Machine Learning Cookbook" by McClure, Packt, 2017-02, 370 pp, $30"Building Machine Learning Projects with TensorFlow" by Bonnin, Packt, 2016-11, 291 pp, $35"Getting Started with TensorFlow" by Zaccone, Packt, 2016-07, 180 pp, $35I reviewed the doc on tensorflow.org - including the doc for older releases - then started looking at books. One week later, I am still not done, but have winnowed out some options. The books by Van Boxel and Zaccone are out - these are brief (and not up-to-date) introductions, costing as much as more substantial titles. The books by Karim, and by Zacone and Karim, are out as a matter of principle, or of my personal distaste for plagiarism. The book by Bonnin offers unremarkable content spoiled by remarkably poor English and a myriad of typos - out."Neural Network Programming with TensorFlow" is the next casualty. I have read only a few pages, and, oh boy, I don't want to go further - it's really poor, I-am-concerned-about-authors'-sanity poor. (Yes, I am talking about that "Environment setup" section in the middle of "Linear algebra" (!) chapter. The only other pages I have glimpsed were the table of contents, and it resembled Karim's). Call me trigger-happy, but I believe in cutting losses early. Moving on…

Neural Network Programming with TensorFlow: Unleash the power of TensorFlow to train efficient neural networks

What do you get with Print?

Neural Network Programming with TensorFlow

Deep Feedforward Networks

Defining feedforward networks

Understanding backpropagation

Implementing feedforward networks with TensorFlow

Analyzing the Iris dataset

Implementing feedforward networks with images

Defining feedforward networks

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