Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

Installing and configuring TensorFlow


You can install and use TensorFlow on a number of platforms such as Linux, macOS, and Windows. Moreover, you can also build and install TensorFlow from the latest GitHub source of TensorFlow. Furthermore, if you have a Windows machine, you can install TensorFlow via native pip or Anacondas. TensorFlow supports Python 3.5.x and 3.6.x on Windows.

In addition, Python 3 comes with the pip3 package manager, which is the program you will use to install TensorFlow. Therefore, you do not need to install pip if you are using this Python version. From our experience, even if you have NVIDIA GPU hardware integrated on your machine, it would be worth installing and trying the CPU-only version first and if you don't experience good performance, you should switch to GPU support then.

The GPU–enabled version of TensorFlow has several requirements such as 64–bit Linux, Python 2.7 (or 3.3+ for Python 3), NVIDIA CUDA® 7.5 or higher (CUDA 8.0 required for Pascal GPUs), and...