Apache Spark Deep Learning Advanced Recipes [Video]

More Information
Learn
  • Organize dataframes for deep learning evaluation
  • Apply testing and training modeling to ensure accuracy
  • Access readily available code that may be reusable
  • Plot and visualize the images 
  • Train the LSTM model
  • Manipulate and merge the MovieLens datasets
About

In this video course, you’ll work through specific recipes to generate outcomes for deep learning algorithms—without getting bogged down in theory. From using LSTMs in generative networks to creating a movie recommendation engine, this course tackles both common and not so common problems so you can perform deep learning in a distributed environment.
In addition, you’ll get access to deep learning code within Spark that you can reuse to answer similar problems or tweak to answer slightly different problems. You’ll learn how to predict real estate value using XGBoost. You’ll also explore how to create a movie recommendation engine using popular libraries such as TensorFlow and Keras. By the end of the course, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark.

The code bundle for this video course is available at https://github.com/PacktPublishing/Advanced-Apache-spark-Deep-learning-recipes

Style and Approach

This course includes practical, easy-to-understand solutions on how you can implement the popular deep learning libraries such as TensorFlow and Keras to train your deep learning models on Apache Spark.

Features
  • Discover practical recipes for distributed deep learning with Apache Spark
  • Predict real estate value using XGBoost
  • Create and visualize Word Vectors using Word2Vec
  • Evaluate the recommendation engine’s accuracy
Course Length 1 hours 35 minutes
ISBN 9781789955309
Date Of Publication 31 Oct 2018

Authors

Amrith Ravindra

Amrith Ravindra is a machine learning enthusiast who holds degrees in electrical and industrial engineering. While pursuing his master’s, he dove deeper into the world of machine learning and developed a love for data science. Graduate-level courses in engineering gave him the mathematical background to launch himself into a career in machine learning. He met Ahmed Sherif at a local data science meetup in Tampa. They decided to put their brains together to write a book on their favorite machine learning algorithms. He hopes this book will help him achieve his ultimate goal of becoming a data scientist and actively contributing to machine learning.

Ahmed Sherif

Ahmed Sherif is a data scientist who has worked with data in various roles since 2005. He started off with BI solutions and transitioned to data science in 2013. In 2016, he obtained a master's in Predictive Analytics from Northwestern University, where he studied the science and application of machine learning and predictive modeling using both Python and R. Lately, he has been developing machine learning and deep learning solutions on the cloud using Azure. In 2016, he published his first book, Practical Business Intelligence. He currently works as a Technology Solution Profession in Data and AI for Microsoft.