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)

LSTM networks

Long Short Term Memory (LSTM) is a special Recurrent Neural Network architecture, which was originally conceived by Hochreiter and Schmidhuber in 1997. This type of neural network has been recently rediscovered in the context of deep learning, because it is free from the problem of vanishing gradients, and offers excellent results and performance. The networks that are LSTM-based are ideal for prediction and classification of temporal sequences, and are replacing many traditional approaches to deep learning.

LSTM is a network that is composed of cells (LSTM blocks) linked to each other. Each LSTM block contains three types of gate: Input gate, Output gate, and Forget gate, respectively, which implement the functions of writing, reading, and resetting on the cell memory. These gates are not binary, but analogical (generally managed by a sigmoidal activation function mapped in the range [0, 1], where...