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)

Implementing a five-layer neural network

The following implementation increases the network complexity by adding four layers before the softmax layer. To determine the appropriate size of the network, that is, the number of hidden layers and the number of neurons per layer, generally we rely on general empirical criteria, the personal experience, or appropriate tests.

The following table summarizes the implemented network architecture, it shows the number of neurons per layer and the respective activation functions:

Layer Number of neurons Activation function
First L = 200 sigmoid
Second M = 100 sigmoid
Third N = 60 sigmoid
Fourth O = 30 sigmoid
Fifth 10 softmax

The transfer function for the first four layers is the sigmoid function; the last layer of the transfer function is always the softmax since the output of the network must express a probability for the input digit. In general, the number...