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

What's new from TensorFlow v1.6 forwards?


In 2015, Google made TensorFlow open source, including all of its reference implementation. All of the source code was made available on GitHub under the Apache 2.0 license. Since then, TensorFlow has been widely adopted in academia and industrial research, and the most stable version, 1.6, has recently been released with a unified API.

It is important to note that the APIs in TensorFlow 1.6 (and higher) are not all backward compatible for pre v1.5 code. This means that some programs that worked on pre v1.5 will not necessarily work on TensorFlow 1.6.

Now let us see the new and exciting features that TensorFlow v1.6 has.

Nvidia GPU support optimized

From TensorFlow v1.5, prebuilt binaries are now built against CUDA 9.0 and cuDNN 7. However, from v1.6's release, TensorFlow prebuilt binaries use AVX instructions, which may break TensorFlow on older CPUs. Nevertheless, since v1.5, an added support for CUDA on NVIDIA Tegra devices has been available.

Introducing...