Develop industrial solutions based on deep learning models with Apache Spark
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.
About the Author
Tomasz Lelek is a Software Engineer who programs mostly in Java and Scala. He has worked with Spark API and the ML API for the past five years and has production experience in processing petabytes of data.
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. He was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and the Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.
- Basics of Machine Learning
- 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
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