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

Environment setup

It is best to use an IDE such as PyCharm to edit Python code; it provides faster development tools and coding assistance. Code completion and inspection makes coding and debugging faster and simpler, ensuring that you focus on the end goal of programming neural networks. 

TensorFlow provides APIs for multiple languages: Python, C++, Java, Go, and so on. We will download a version of TensorFlow that will enable us to write the code for deep learning models in Python. On the TensorFlow installation website, we can find the most common ways and latest instructions to install TensorFlow using virtualenv, pip, and Docker.

The following steps describe how to set up a local development environment:

  1. Download the Pycharm community edition.
  2. Get the latest Python version on Pycharm.
  3. Go to Preferences, set up the python interpreter, and install the latest version of TensorFlow:
  1. TensorFlow will now appear in the installed packages list. Click on OK. Now test your installation with a program...