As a Windows user interested in learning to use GPUs for deep learning I have been frustrated with the available software and books for a couple of years. Most books seem to have been written on Linux systems and then you are told that the code should work fine with Windows - which is not always so. Also several deep learning systems have not been set up for Windows yet. So when I first read about TensorFlow I decided to give it a try: I got this book and one other. Final evaluation first: I gave it 4 stars because the code worked for me (more accurately: much, not all of the code worked), but the situation was better than other books hence 4 stars.

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.