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

Pain Point #1: Importing MNIST images


One of the most common datasets used for image classification is the MNIST dataset, which is composed of thousands of samples of handwritten digits. The Modified National Institute of Standards and Technology (MNIST) is, according to Yann LeCun, Corinna Cortes, and Christopher J.C. Burges, useful for the following reasons:

Note

It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

There are several methods to import the MNIST images into our Jupyter notebook. We will cover the following two methods in this chapter:

  1. Directly through the TensorFlow library
  2. Manually through the MNIST website

Note

One thing to note is that we will be primarily using MNIST images as our example of how to improve performance within a convolutional neural network. All of these techniques that will be applied on MNIST images can be applied to any image that...