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)

Dropout optimization

During the learning phase, the connections with the next layer can be limited to a subset of neurons to reduce the weights to be updated, this learning optimization technique is called dropout. The dropout is therefore a technique used to decrease the overfitting within a network with many layers and/or neurons. In general, the dropout layers are positioned after the layers that possess a large amount of trainable neurons.

This technique allows setting to 0, and then excluding the activation of a certain percentage of the neurons of the preceding layer. The probability that the neuron's activation is set to 0 is indicated by the dropout ratio parameter within the layer, via a number between 0 and 1: in practice the activation of a neuron is held with probability equal to the dropout ratio, otherwise it is discarded, that is, set to 0.

The neurons by this transaction do not affect, therefore...