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

TFLearn


TFLearn is a library that wraps a lot of new TensorFlow APIs with the nice and familiar scikit-learn API.

TensorFlow is all about a building and executing graphs. This is a very powerful concept, but it is also cumbersome to start with.

Looking under the hood of TF.Learn, we just used three parts:

  • layers: A set of advanced TensorFlow functions that allow us to easily build complex graphs, from fully connected layers, convolution, and batch norm to losses and optimization.

  • graph_actions:  A set of tools to perform training, evaluating, and running inference on TensorFlow graphs.

  • Estimator: This packages everything into a class that follows scikit-learn interface and provides a way to easily build and train custom TensorFlow models.

Installation

To install TFLearn, the easiest way is to run the following command:

pip install git+https://github.com/tflearn/tflearn.git

For the latest stable version, use this command:

pip install tflearn

Otherwise, you can also install it from source by...