Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.
This course allows you to learn how Dimensionality Reduction, a category of unsupervised machine learning techniques, is used to reduce the number of features in a dataset. Dimension reduction can also be used to group similar variables together.
In this course, you'll learn to create basic machine learning models that you train to recognize sounds of dogs, cats, and birds. You will also integrate visual recognition to identify images of these animals. Through the course of your learning, you can also build a basic user-interface in Node-RED that shows the results of the predictions for both sound and images.
Apache SystemML is a declarative style language designed for large-scale machine learning. It provides automatic generation of optimized runtime plans ranging from single-node, to in-memory, to distributed computations on Apache Hadoop and Apache Spark. SystemML algorithms are expressed in R-like or Python-like syntax that includes linear algebra primitives, statistical functions and ML-specific constructs.