This is the code repository for Apache Spark Deep Learning Recipes [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- 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
To fully benefit from the coverage included in this course, you will need:
This video course is for anyone with a basic understanding of machine learning and big data concepts who is looking to expand their understanding through a top-down rather than a bottom-up approach. Anyone without previous programming experience, especially with Python, can easily implement the algorithms in this video course by following the recipes step by step as instructed.
This course has the following software requirements:
Ubuntu Desktop 16.04.3,
Minimum 2GHz Dual-Core Processor,
Minimum 2GB system memory,
Minimum 25 GB of hard drive space.