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

A general overview of TensorFlow


TensorFlow is an open source framework from Google for scientific and numerical computation using data flow graphs that stand for TensorFlow's execution model. The data flow graphs used in TensorFlow help ML experts to perform more advanced and intensive training on their data to develop DL and predictive analytics models.

As the name implies, TensorFlow includes operations that are performed by neural networks on multidimensional data arrays, that is, flow of tensors. Nodes in a flow graph correspond to mathematical operations, that is, addition, multiplication, matrix factorization, and so on; whereas, edges correspond to tensors that ensure communication between edges and nodes – that is, data flow and control flow. This way, TensorFlow provides some widely used and robustly implemented linear models and DL algorithms.

You can perform numerical computations on a CPU. However, with TensorFlow, it is also possible to distribute the training among multiple...