In the last 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. It will also cover the “art” part of data science: what is data science, how data science project flow, data science types v.s needs, how to organize a data science team and where it belongs in the organization. The course is for audience with a statistical background. The hands-on sessions use Databricks community edition cloud platform.
(1) Introduction to Deep Neural Network, Convolutional Neural Network and Recurrent Neural Networks and their applications;
(2) Deep learning examples using TensorFlow through R keras package;
(3) Big data platform using Spark through R sparklyr package.
| Topic | Time |
|---|---|
| Introduction to Data Science | 8:15 - 8:45 |
| Deep Learning 1 | 8:45 - 10:15 |
| Big Data Cloud Platform | 10:15 - 10:30 |
| Break | 10:30 - 10:45 |
| Deep Learning 1 Hands-on | 10:45 - 11:00 |
| Deep Learning 2 | 11:00 - 11:30 |
| Deep Learning 3 | 11:30 - 12:00 |
| Deep Learning Hands-on Session | 12:00 - 12:15 |
- Course homepage: https://idad2019.netlify.com/
- Databrick free community edition account
- Perceptron notebook
- Adaline notebook
- Feedforward neural network notebook
- Convolutional neural network notebook
- Recurrent neural network notebook
- Big Data Platform notebook
- Data preprocessing notebook
- Data wrangling notebook
- Industry recommendations for academic data science programs
- Deep Learning Using R, François Chollet with J. J. Allaire, ISBN 9781617295546 (2018)
- Python Machine Learning by Sebastian Raschka, ISBN-13: 978-1787125933 (2018)
- https://keras.rstudio.com/
- http://spark.rstudio.com/
- https://databricks.com/spark/about
- https://github.com/onnx/onnx