This is the code repository for Apache Spark Deep Learning Advanced Recipes [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
In this video course, you’ll work through specific recipes to generate outcomes for deep learning algorithms—without getting bogged down in theory. From using LSTMs in generative networks to creating a movie recommendation engine, this course tackles both common and not so common problems so you can perform deep learning in a distributed environment. In addition, you’ll get access to deep learning code within Spark that you can reuse to answer similar problems or tweak to answer slightly different problems. You’ll learn how to predict real estate value using XGBoost. You’ll also explore how to create a movie recommendation engine using popular libraries such as TensorFlow and Keras. By the end of the course, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark.
- Organize dataframes for deep learning evaluation
- Apply testing and training modeling to ensure accuracy
- Access readily available code that may be reusable
- Plot and visualize the images
- Train the LSTM model
- Manipulate and merge the MovieLens datasets
To fully benefit from the coverage included in this course, you will need:
This video course is for anyone without previous programming experience, especially with Python. You can easily implement the recipes by following them step by step as instructed. Each code block performs one particular function or executes on the action in mining, manipulating, transforming, and fitting data to deep learning models.
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.