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Deep Learning with TensorFlow

Deep Learning with TensorFlow

By : Zaccone, Karim
3 (4)
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Deep Learning with TensorFlow

Deep Learning with TensorFlow

3 (4)
By: Zaccone, 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 (13 chapters)
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12
Index

Chapter 6. Recurrent Neural Networks

A RNN is a class of ANN where connections between units form a directed cycle. RNNs make use of information from the past. That way, they can make predictions in data with high temporal dependencies. This creates an internal state of the network, which allows it to exhibit dynamic temporal behavior. In this chapter, we will develop several real-life predictive models, using RNNs and their architectural variants, to make predictive analytics easier.

First, we will provide some theoretical background of RNNs. Then we will look at a few examples that will show a systematic way of implementing predictive models for image classification, sentiment analysis of movies, and spam predictions for Natural Language Processing (NLP).

Then we will show how to develop predictive models for time series data. Finally, we will see a how to develop a LSTM network for solving more advanced problems, such as human activity recognition.

Concisely, the following topics...

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