Book Image

Apache Spark Deep Learning Cookbook

By : Ahmed Sherif, Amrith Ravindra
Book Image

Apache Spark Deep Learning Cookbook

By: Ahmed Sherif, Amrith Ravindra

Overview of this book

Organizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they’re looking to gain faster and more powerful insights from their data. With this book, you’ll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark. Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You’ll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning. By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.
Table of Contents (21 chapters)
Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface
Index

Sequential workings of RNNs


Recurrent neural networks are a type of artificial neural network designed to recognize and learn patterns in sequences of data. Some of the examples of such sequential data are:

  • Handwriting
  • Text such as customer reviews, books, source code, and so on
  • Spoken word / Natural Language
  • Numerical time series / sensor data
  • Stock price variation data

Getting ready

In recurrent neural networks, the hidden state from the previous time step is fed back into the network at the next time step, as shown in the following diagram:

Basically, the upward facing arrows going into the network represent the inputs (matrices/vectors) to the RNN at each time step, while the upward-facing arrows coming out of the network represent the output of each RNN unit. The horizontal arrows indicate the transfer of information learned in a particular time step (by a particular neuron) onto the next time step.

Note

More information about using RNNs can be found at :https://deeplearning4j.org/usingrnns

How...