Mastering Deep Learning using Apache Spark [Video]

More Information
Learn
  • Configure a Convolutional Neural Network (CNN) to extract value from images
  • Create a deep network with multiple layers to perform computer vision
  • Classify speech and audio data
  • Leverage RNN and LSTMs for video classification for hospital data
  • Improve cybersecurity with deep reinforcement learning
  • Use a generative adversarial network for training
  • Create highly distributed algorithms using Spark
About

Deep learning has solved tons of interesting real-world problems in recent years. Apache Spark has emerged as the most important and promising machine learning tool and currently a stronger challenger of the Hadoop ecosystem. In this course, you’ll learn about the major branches of AI and get familiar with several core models of Deep Learning in its natural way.

You’ll begin with building deep learning networks to deal with speech data and explore tricks to solve NLP problems and classify video frames using RNN and LSTMs. You’ll also learn to implement the anomaly detection model that leverages reinforcement learning techniques to improve cyber security.

Moving on, you’ll explore some more advanced topics by performing prediction classification on image data using the GAN encoder and decoder. Then you’ll configure Spark to use multiple workers and CPUs to distribute your Neural Network training. Finally, you’ll track progress, solve the most common problems in your neural network, and debug your models that run within the distributed Spark engine.

Style and Approach

This course takes a practical approach to networking and will get you familiar with several core models. It will help you implement deep learning models like CNN, RNN, LTSMs on Spark and get hands-on experience of what it takes and a general feeling of the complexity we are dealing with.

Features
  • Create robust deep learning pipelines that leverage Apache Spark for fast execution
  • Perform advanced classification of non-structured data using Deep Learning (DL) techniques
  • Adapt the Neural Network configuration and decide when to add the next layer to achieve the results needed
Course Length 2 hours 3 minutes
ISBN 9781788292511
Date Of Publication 15 Apr 2019

Authors

Tomasz Lelek

Tomasz Lelek is a software engineer who programs mostly in Java and Scala. He has worked with the core Java language for the past six years. He has developed multiple production Java software projects that work in a reactive way.

He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. Recently, he was a speaker at conferences in Poland, at JDD (Java Developers Day), and at Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.

He is a co-founder of initLearn, an e-learning platform that was built with the Java language.

He has also written articles about everything related to the Java world.