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)

TensorBoard

When training a neural network, it may be useful to keep track of network parameters, typically the inputs and outputs from the nodes, so you can see whether your model is learning such verifying after each training step if the function error is minimized or not. Of course, writing code to display the behavior of the network during the learning phase, it can be not easy.

Installing TensorBoard is pretty straight forward. Just issue the following command on Terminal (On Ubuntu for Python 2.7+):
$ sudo pip install tensorboard

Fortunately, TensorFlow provides TensorBoard which is a framework designed for analysis and debugging of neural network models. TensorBoard uses the so-called summaries to view the parameters of the model; once a TensorFlow code is executed, we can call TensorBoard to view summaries in a graphical user interface (GUI).

Furthermore, TensorBoard can be used to display and study the...