Book Image

Deep Learning with TensorFlow

By : Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
Book Image

Deep Learning with TensorFlow

By: Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy

Overview of this book

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.
Table of Contents (11 chapters)

TFLearn

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

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

Looking under the hood of TFLearn, we used just three parts:

  • Layers: This is a set of advanced TensorFlow functions that allows you to easily build complex graphs, from fully connected layers, convolution, and batch norm, to losses and optimization.
  • Graph actions: This is a set of tools to perform training and evaluating, and run inference on TensorFlow graphs.
  • Estimator: This packages everything in a class that follows the scikit-learn interface, and provides a way to easily build and train custom TensorFlow models. Subclasses of Estimator, such as linear classifier, linear regressor, DNN classifier, and so on ,  are pre-packaged models similar to scikit...