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.
Table of Contents (17 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

An overview and the intuition of CNN

CNN consists of multiple layers of convolutions, polling and finally fully connected layers. This is much more efficient than pure feedforward networks we discussed in Chapter 2, Deep Feedforward Networks.

The preceding diagram takes images through Convolution Layer | Max Pooling | Convolution | Max Pooling | Fully Connected Layers this is an CNN architecture

Single Conv Layer Computation

Let's first discuss what the conv layer computes intuitively. The Conv layer's parameters consist of a set of learnable filters (also called tensors). Each filter is small spatially (depth, width, and height), but extends through the full depth of the input volume (image). A filter on the first layer of a ConvNet typically has a size of 5 x 5 x 3 (that is, five pixels width and height, and three for depth, because images have three depths for color channels). During the forward pass, filters slide (or convolve) across the width and height of the input volume and compute...