Book Image

Hands-On Deep Learning with Apache Spark

By : Guglielmo Iozzia
Book Image

Hands-On Deep Learning with Apache Spark

By: Guglielmo Iozzia

Overview of this book

Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases.
Table of Contents (19 chapters)
Appendix A: Functional Programming in Scala
Appendix B: Image Data Preparation for Spark

Hands-on NLP with TensorFlow

In this section, we are going to use TensorFlow (Python) to do DL sentiment analysis using the same Large Movie Review Dataset as for the first example in the previous section. Prerequisites for this example are Python 2.7.x, the PIP package manager, and Tensorflow. The Importing Python Models in the JVM with DL4J section in Chapter 10, Deploying on a Distributed System, covers the details of setting up the required tools. We are also going to use the TensorFlow hub library (https://www.tensorflow.org/hub/), which has been created for reusable ML modules. It needs to be installed through pip, as follows:

pip install tensorflow-hub

The example also requires the pandas (https://pandas.pydata.org/) data analysis library, as follows:

pip install pandas

Import the necessary modules:

import tensorflow as tf
import tensorflow_hub as hub
import os
import...