This is the code repository for Mastering Deep Learning using Apache Spark [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Deep learning has been solving 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 will learn the major branches of AI and get familiar with several core models of Deep Learning in its natural way.
You will begin with building DL networks to deal with speech data and explore tricks to solve NLP problems, guess words meaning using transfer learning techniques, and classify video frames using RNN and LSTM’s. You will also learn to implement the Anomaly Detection model that leverages Reinforcement Learning techniques to improve cybersecurity.
Moving further you will explore advanced topics of GANs by performing prediction classification of image data using GAN encoder and decoder. Then you will configure Spark to use multiple workers and CPUs to distribute your Neural Network Training. Finally, you will track progress, solve most common problems and debug your models that run within distributed Spark engine.
- 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
To fully benefit from the coverage included in this course, you will need:
• Prior working knowledge of the Java Language, Scala, Apache Spark<br/>
• Familiarity with Git and GitHub for source control<br/>
This course has the following software requirements:
• IntelliJ IDEA<br/>
• Java JDK 8 or later<br/>
• Scala SDK<br/>
This course has been tested on the following system configuration:
• OS: MacOSX
• Processor: I7 2.8
• Memory: 16GB
• Hard Disk Space: 200MB
• Video Card: 256MB Video Memory