alicehua11/v3

W251 - Deep Learning in the Cloud and at the Edge

This hands-on course introduces data scientists to technologies related to building and operating live, high throughput deep learning applications running on powerful servers in the cloud as well on smaller and lower power devices at the edge of the network. The material of the class is a set of practical approaches, code recipes, and lessons learned. It is based on the latest developments in the industry and industry use cases as opposed to pure theory.

See hw01 for edge device requirements.

The syllabus and homeworks are as follows,

Week	Content
01	Introduction and Overview
02	Clouds, Infrastructure, and Machine Learning Cloud Services
03	Introduction to Containers
04	Deep Learning 101
05	Deep Learning Frameworks
06	Optimizing Models for the Edge and GStreamer
07	Deep Learning 201
08	Datasets and Dataset Processing
09	HPC, MPI, and Multinode/MultiGPU (MNMG) Training
10	Generative Adversarial Networks (GANs)
11	Deep Reinforcement Learning
12	Speech, Natural Language Processing, and Conversational Design
13	AI and DL: Applying AI to Real World Applications