Apache Spark Deep Learning Cookbook: Access to 80 enriched recipes that streamline deep learning in a distributed environment with Apache Spark

By 作者: Ahmed Sherif – Amrith Ravindra

ISBN-10 书号: 1788474228

ISBN-13 书号: 9781788474221

Release Finelybook 出版日期: 2018-08-09

pages 页数: 564

$49.99

Book Description to Finelybook sorting

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.

**Contents**

1: SETTING UP SPARK FOR DEEP LEARNING DEVELOPMENT

2: CREATING A NEURAL NETWORK IN SPARK

3: PAIN POINTS OF CONVOLUTIONAL NEURAL NETWORKS

4: PAIN POINTS OF RECURRENT NEURAL NETWORKS

5: PREDICTING FIRE DEPARTMENT CALLS WITH SPARK ML

6: USING LSTMS IN GENERATIVE NETWORKS

7: NATURAL LANGUAGE PROCESSING WITH TF-IDF

8: REAL ESTATE VALUE PREDICTION USING XGBOOST

9: PREDICTING APPLE STOCK MARKET COST WITH LSTM

10: FACE RECOGNITION USING DEEP CONVOLUTIONAL NETWORKS

11: CREATING AND VISUALIZING WORD VECTORS USING WORD2VEC

12: CREATING A MOVIE RECOMMENDATION ENGINE WITH KERAS

13: IMAGE CLASSIFICATION WITH TENSORFLOW ON 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

Authors

Ahmed primarily

Ahmed primarily works with TensorFlow projects to do with sentiment and facial recognition from twitter posts and profiles. He also used TensorFlow to help identify fraud activity with credit unions to determine bank statements that are false and counterfeit dollar bills.

In 2016, he completed a Master’s in Predictive Analytics from Northwestern University, where he focused on ML and predictive modeling techniques using SAS, R, and Python. As a data scientist, Ahmed strives to fuse predictive capabilities into business intelligence solutions.

Amrith Ravindra

Amrith completed his bachelor’s in Electrical Engineering from BMS Institute of Technology, Bangalore, in 2015. He is currently studying at University of South Florida’s graduate program in Industrial and Systems Engineering where he had mathematical courses like Statistical Design Models, Probabilistic Systems Analysis, Optimization in Operations Research as well as some really interesting electives to learn concepts in analytics of big data.

His dream job would be something that would allow him to combine his knowledge of data science with his passion for sports. ESPN perhaps?

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