Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

Summary


LSTM networks are equipped with special hidden units, called memory cells, whose purpose is to remember the previous input for a long time. These cells take, at each instant of time, the previous state and the current input of the network as input. By combining them with the current contents of memory, and deciding what to keep and what to delete from memory with a gating mechanism by other units, LSTM has proved to be very useful and an effective way of learning long-term dependency.

In this chapter, we discussed RNNs. We saw how to make predictions with data that has a high temporal dependency. We saw how to develop several real-life predictive models that make the predictive analytics easier using RNNs and the different architectural variants. We started the chapter with a theoretical background of RNNs.

Then we looked at a few examples that showed a systematic way of implementing predictive models for image classification, sentiment analysis of movies and products, and spam prediction...