All knowledge is, in final analysis, history. All sciences are, in the abstract, mathematics. All judgements are, in their rationale, statistics. -- C. R. Rao.
In the past couple of years, deep learning has gained traction in many areas. It becomes an essential tool in data scientist’s toolbox. In this course, students will develop a clear understanding of the big data cloud platform, technical skills in data sciences and machine learning, the motivation and use cases of deep learning through hands-on exercises. We will also cover the “art” part of data science: data science project flow, general pitfalls in data science and machine learning, and soft skills to effectively communicate with business stakeholders. The course is for audience with statistics background. We use real-world data science and machine learning problems to illustrate data science workflow, pitfalls, and soft skills. The hands-on sessions use Databricks community edition cloud platform.
(1) Big data platform using Spark through R sparklyr package;
(2) Introduction to Deep Neural Network, Convolutional Neural Network and Recurrent Neural Networks and their applications;
(3) Deep learning examples using TensorFlow through R keras package.