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Apache Spark Deep Learning Cookbook电子书

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作       者:Ahmed Sherif,Amrith Ravindra

出  版  社:Packt Publishing

出版时间:2018-07-13

字       数:36.3万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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A solution-based guide to put your deep learning models into production with the power of Apache Spark Key Features * Discover practical recipes for distributed deep learning with Apache Spark * Learn to use libraries such as Keras and TensorFlow * Solve problems in order to train your deep learning models on Apache Spark Book Description With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed. With the help of the Apache Spark Deep Learning Cookbook, you’ll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you’ll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you’ll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras. By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark. What you will learn * Set up a fully functional Spark environment * Understand practical machine learning and deep learning concepts * Apply built-in machine learning libraries within Spark * Explore libraries that are compatible with TensorFlow and Keras * Explore NLP models such as Word2vec and TF-IDF on Spark * Organize dataframes for deep learning evaluation * Apply testing and training modeling to ensure accuracy * Access readily available code that may be reusable Who this book is for If you’re looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python is a plus.
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Title Page

Copyright and Credits

Apache Spark Deep Learning Cookbook

Packt Upsell

Why subscribe?

PacktPub.com

Foreword

Contributors

About the authors

About the reviewers

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Conventions used

Sections

Getting ready

How to do it...

How it works...

There's more...

See also

Get in touch

Reviews

Setting Up Spark for Deep Learning Development

Introduction

Downloading an Ubuntu Desktop image

Getting ready

How to do it...

How it works...

There's more...

See also

Installing and configuring Ubuntu with VMWare Fusion on macOS

Getting ready

How to do it...

How it works...

There's more...

See also

Installing and configuring Ubuntu with Oracle VirtualBox on Windows

Getting ready

How to do it...

How it works...

There's more...

See also

Installing and configuring Ubuntu Desktop for Google Cloud Platform

Getting ready

How to do it...

How it works...

There's more...

See also

Installing and configuring Spark and prerequisites on Ubuntu Desktop

Getting ready

How to do it...

How it works...

There's more...

See also

Integrating Jupyter notebooks with Spark

Getting ready

How to do it...

How it works...

There's more...

See also

Starting and configuring a Spark cluster

Getting ready

How to do it...

How it works...

There's more...

See also

Stopping a Spark cluster

How to do it...

How it works...

There's more...

Creating a Neural Network in Spark

Introduction

Creating a dataframe in PySpark

Getting ready

How to do it...

How it works...

There's more...

See also

Manipulating columns in a PySpark dataframe

Getting ready

How to do it...

How it works...

There's more...

Converting a PySpark dataframe to an array

Getting ready

How to do it...

How it works...

There's more...

See also

Visualizing an array in a scatterplot

Getting ready

How to do it...

How it works...

There's more...

See also

Setting up weights and biases for input into the neural network

Getting ready

How to do it...

How it works...

There's more...

See also

Normalizing the input data for the neural network

Getting ready

How to do it...

How it works...

See also

Validating array for optimal neural network performance

Getting ready

How to do it...

How it works...

There's more...

See also

Setting up the activation function with sigmoid

Getting ready

How to do it...

How it works...

See also

Creating the sigmoid derivative function

Getting ready

How to do it...

How it works...

See also

Calculating the cost function in a neural network

Getting ready

How to do it...

How it works...

There's more...

See also

Predicting gender based on height and weight

Getting ready

How to do it...

How it works...

See also

Visualizing prediction scores

Getting ready

How to do it...

How it works...

Pain Points of Convolutional Neural Networks

Introduction

Pain Point #1: Importing MNIST images

Getting ready

How to do it...

How it works...

There's more...

See also

Pain Point #2: Visualizing MNIST images

Getting ready

How to do it...

How it works...

There's more...

See also

Pain Point #3: Exporting MNIST images as files

Getting ready

How to do it...

How it works...

There's more...

See also

Pain Point #4: Augmenting MNIST images

Getting ready

How to do it...

How it works...

There's more...

See also

Pain Point #5: Utilizing alternate sources for trained images

Getting ready

How to do it...

How it works...

There's more...

See also

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

Getting ready

How to do it...

How it works...

There's more...

See also

Pain Points of Recurrent Neural Networks

Introduction

Introduction to feedforward networks

Getting ready

How to do it...

How it works...

There's more...

See also

Sequential workings of RNNs

Getting ready

How to do it...

How it works...

There's more...

See also

Pain point #1 – The vanishing gradient problem

Getting ready

How to do it...

How it works...

There's more...

See also

Pain point #2 – The exploding gradient problem

Getting ready

How to do it...

How it works...

There's more...

See also

Sequential working of LSTMs

Getting ready

How to do it...

How it works...

There's more...

See also

Predicting Fire Department Calls with Spark ML

Introduction

Downloading the San Francisco fire department calls dataset

Getting ready

How to do it...

How it works...

There's more...

See also

Identifying the target variable of the logistic regression model

Getting ready

How to do it...

How it works...

There's more...

See also

Preparing feature variables for the logistic regression model

Getting ready

How to do it...

How it works...

There's more...

See also

Applying the logistic regression model

Getting ready

How to do it...

How it works...

There's more...

See also

Evaluating the accuracy of the logistic regression model

Getting ready

How to do it...

How it works...

There's more...

See also

Using LSTMs in Generative Networks

Introduction

Downloading novels/books that will be used as input text

Getting ready

How to do it...

How it works...

There's more...

See also

Preparing and cleansing data

Getting ready

How to do it...

How it works...

See also

Tokenizing sentences

Getting ready

How to do it...

How it works...

There's more...

Training and saving the LSTM model

Getting ready

How to do it...

How it works...

There's more...

See also

Generating similar text using the model

Getting ready

How to do it...

How it works...

There's more...

See also

Natural Language Processing with TF-IDF

Introduction

Downloading the therapy bot session text dataset

Getting ready

How it works...

How to do it...

There's more...

Analyzing the therapy bot session dataset

Getting ready

How to do it...

How it works...

Visualizing word counts in the dataset

Getting ready

How to do it...

How it works...

See also

Calculating sentiment analysis of text

Getting ready

How to do it...

How it works...

See also

Removing stop words from the text

Getting ready

How to do it...

How it works...

See also

Training the TF-IDF model

Getting ready

How to do it...

How it works...

There's more...

See also

Evaluating TF-IDF model performance

Getting ready

How to do it...

How it works...

See also

Comparing model performance to a baseline score

How to do it...

How it works...

See also

Real Estate Value Prediction Using XGBoost

Downloading the King County House sales dataset

Getting ready

How to do it...

How it works...

There's more...

See also

Performing exploratory analysis and visualization

Getting ready

How to do it...

How it works...

There's more...

See also

Plotting correlation between price and other features

Getting ready

How to do it...

How it works...

There's more...

See also

Predicting the price of a house

Getting ready

How to do it...

How it works...

There's more...

See also

Predicting Apple Stock Market Cost with LSTM

Downloading stock market data for Apple

Getting ready

How to do it...

How it works...

There's more...

See also

Exploring and visualizing stock market data for Apple

Getting ready

How to do it...

How it works...

There's more...

See also

Preparing stock data for model performance

Getting ready

How to do it...

How it works...

There's more...

See also

Building the LSTM model

Getting ready

How to do it...

How it works...

See also

Evaluating the model

Getting ready

How to do it...

How it works...

See also

Face Recognition Using Deep Convolutional Networks

Introduction

Downloading and loading the MIT-CBCL dataset into the memory

Getting ready

How to do it...

How it works...

There's more...

See also

Plotting and visualizing images from the directory

Getting ready

How to do it...

How it works...

There's more...

See also

Preprocessing images

Getting ready

How to do it...

How it works...

There's more...

See also

Model building, training, and analysis

Getting ready

How to do it...

How it works...

There's more...

See also

Creating and Visualizing Word Vectors Using Word2Vec

Introduction

Acquiring data

Getting ready

How to do it...

How it works...

There's more...

See also

Importing the necessary libraries

Getting ready

How to do it...

How it works...

There's more...

See also

Preparing the data

Getting ready

How to do it...

How it works...

There's more...

See also

Building and training the model

Getting ready

How to do it...

How it works...

There's more...

See also

Visualizing further

Getting ready

How to do it...

How it works...

See also

Analyzing further

Getting ready

How to do it...

How it works...

See also

Creating a Movie Recommendation Engine with Keras

Introduction

Downloading MovieLens datasets

Getting ready

How to do it...

How it works...

There's more...

See also

Manipulating and merging the MovieLens datasets

Getting ready

How to do it...

How it works...

There's more...

See also

Exploring the MovieLens datasets

Getting ready

How to do it...

How it works...

There's more...

See also

Preparing dataset for the deep learning pipeline

Getting ready

How to do it...

How it works...

There's more...

See also

Applying the deep learning model with Keras

Getting ready

How to do it...

How it works...

There's more...

See also

Evaluating the recommendation engine's accuracy

Getting ready

How to do it...

How it works...

See also

Image Classification with TensorFlow on Spark

Introduction

Downloading 30 images each of Messi and Ronaldo

Getting ready

How to do it...

How it works...

There's more...

See also

Configuring PySpark installation with deep learning packages

Getting ready

How to do it...

How it works...

There's more...

See also

Loading images on to PySpark dataframes

Getting ready

How to do it...

How it works...

There's more...

See also

Understanding transfer learning

Getting ready

How to do it...

How it works...

There's more...

See also

Creating a pipeline for image classification training

Getting ready

How to do it...

How it works...

There's more...

See also

Evaluating model performance

Getting ready

How to do it...

How it works...

There's more...

See also

Fine-tuning model parameters

Getting ready

How to do it...

How it works...

There's more...

See also

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