Apache Spark Deep Learning Cookbook

Apache Spark Deep Learning Cookbook

By : Ahmed Sherif, Amrith Ravindra

Buy this Book

Apache Spark Deep Learning Cookbook

By: Ahmed Sherif, Amrith Ravindra

Buy this Book

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.

Title Page

Packt Upsell

Foreword

Contributors

Preface

Free Chapter

Setting Up Spark for Deep Learning Development

Introduction

Downloading an Ubuntu Desktop image

Installing and configuring Ubuntu with VMWare Fusion on macOS

Installing and configuring Ubuntu with Oracle VirtualBox on Windows

Installing and configuring Ubuntu Desktop for Google Cloud Platform

Installing and configuring Spark and prerequisites on Ubuntu Desktop

Integrating Jupyter notebooks with Spark

Starting and configuring a Spark cluster

Stopping a Spark cluster

Creating a Neural Network in Spark

Introduction

Creating a dataframe in PySpark

Manipulating columns in a PySpark dataframe

Converting a PySpark dataframe to an array

Visualizing an array in a scatterplot

Setting up weights and biases for input into the neural network

Normalizing the input data for the neural network

Validating array for optimal neural network performance

Setting up the activation function with sigmoid

Creating the sigmoid derivative function

Calculating the cost function in a neural network

Predicting gender based on height and weight

Visualizing prediction scores

Pain Points of Convolutional Neural Networks

Introduction

Pain Point #1: Importing MNIST images

Pain Point #2: Visualizing MNIST images

Pain Point #3: Exporting MNIST images as files

Pain Point #4: Augmenting MNIST images

Pain Point #5: Utilizing alternate sources for trained images

Pain Point #6: Prioritizing high-level libraries for CNNs

Pain Points of Recurrent Neural Networks

Introduction

Introduction to feedforward networks

Sequential workings of RNNs

Pain point #1 – The vanishing gradient problem

Pain point #2 – The exploding gradient problem

Sequential working of LSTMs

Predicting Fire Department Calls with Spark ML

Introduction

Downloading the San Francisco fire department calls dataset

Identifying the target variable of the logistic regression model

Preparing feature variables for the logistic regression model

Applying the logistic regression model

Evaluating the accuracy of the logistic regression model

Using LSTMs in Generative Networks

Introduction

Downloading novels/books that will be used as input text

Preparing and cleansing data

Tokenizing sentences

Training and saving the LSTM model

Generating similar text using the model

Natural Language Processing with TF-IDF

Introduction

Downloading the therapy bot session text dataset

Analyzing the therapy bot session dataset

Visualizing word counts in the dataset

Calculating sentiment analysis of text

Removing stop words from the text

Training the TF-IDF model

Evaluating TF-IDF model performance

Comparing model performance to a baseline score

Real Estate Value Prediction Using XGBoost

Downloading the King County House sales dataset

Performing exploratory analysis and visualization

Plotting correlation between price and other features

Predicting the price of a house

Predicting Apple Stock Market Cost with LSTM

Downloading stock market data for Apple

Exploring and visualizing stock market data for Apple

Preparing stock data for model performance

Building the LSTM model

Evaluating the model

Face Recognition Using Deep Convolutional Networks

Introduction

Downloading and loading the MIT-CBCL dataset into the memory

Plotting and visualizing images from the directory

Preprocessing images

Model building, training, and analysis

Creating and Visualizing Word Vectors Using Word2Vec

Introduction

Acquiring data

Importing the necessary libraries

Preparing the data

Building and training the model

Visualizing further

Analyzing further

Creating a Movie Recommendation Engine with Keras

Introduction

Downloading MovieLens datasets

Manipulating and merging the MovieLens datasets

Exploring the MovieLens datasets

Preparing dataset for the deep learning pipeline

Applying the deep learning model with Keras

Evaluating the recommendation engine's accuracy

Image Classification with TensorFlow on Spark

Introduction

Downloading 30 images each of Messi and Ronaldo

Configuring PySpark installation with deep learning packages

Loading images on to PySpark dataframes

Understanding transfer learning

Creating a pipeline for image classification training

Evaluating model performance

Fine-tuning model parameters

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Model building, training, and analysis

We will use a standard sequential model from the keras library to build the CNN. The network will consist of three convolutional layers, two maxpooling layers, and four fully connected layers. The input layer and the subsequent hidden layers have 16 neurons, while the maxpooling layers contain a pool size of (2,2). The four fully connected layers consist of two dense layers and one flattened layer and one dropout layer. Dropout 0.25 was used to reduce the overfitting problem. Another noveltyof this algorithm is the use of dataaugmentation to fight the overfitting phenomenon. Data augmentation is carried by rotating, shifting, shearing, and zooming the images to different extents to fit the model.

The relu function is used as the activation function in both the input and hidden layers, while the softmax classifier is used in the output layer to classify the test images based on the predicted output.

Getting ready

The network which will be constructed can...

Apache Spark Deep Learning Cookbook

By : Ahmed Sherif, Amrith Ravindra

Apache Spark Deep Learning Cookbook

By: Ahmed Sherif, Amrith Ravindra

Overview of this book

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