Apache Spark Deep Learning Recipes [Video]

More Information
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
About

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.
This video course start offs by explaining the process of developing a neural network from scratch using deep learning libraries such as Tensorflow or Keras. It focuses on the pain points of convolution neural networks. We’ll predict fire department calls with Spark ML and Apple stock market cost with LSTM. We’ll walk you through the steps to classify chatbot conversation data for escalation.
By the end of the video course, you'll have all the basic knowledge about apache spark.
The code bundle for this video course is available at https://github.com/PacktPublishing/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, without getting bogged down in theory.

Features
  • Discover practical recipes for distributed deep learning with Apache Spark
  • Learn to use libraries such as Keras and TensorFlow 
  • Predict Apple stock market cost with the LSTM model
Course Length 1 hour 49 minutes
ISBN 9781789955521
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.

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