Deep Learning Certificate Part II

This course follows on from (and requires completion of) part 1 (and the associated MOOC). It tackles more complex problems that require integrating a number of techniques. This includes both integrating multiple deep learning techniques , as well as combining classic machine learning techniques with deep learning. All methods will be introduced in the context of solving end-to-end real world modeling problems.

Curriculum Details

Prerequisites

  • familiarity with Python, git, and bash
  • familiarity with the content covered in Deep Learning Part 1, version 2, including the fastai library, a high-level wrapper for PyTorch.
  • attend in-person Monday evenings at our Downtown campus
  • commit 10 hours a week to course study

The deep learning, the fastai library, and PyTorch requirements can be fulfilled by:

  • completing the updated, in-person Deep Learning Part 1 course (first offered fall '17)
  • completing all 7 lessons of part 1 of the MOOC at course.fast.ai
Details
Dates March 18 - April 30 (7 weeks)
Schedule 6:30-9 p.m.
on
Mon Mar 18
Mon Mar 25
Wed Apr 3
Wed Apr 10
Wed Apr 17
Tue Apr 23
Tue Apr 30
Location 101 Howard St.
San Francisco, CA
Instructors Jeremy Howard
Continuing Education Units 1.5
Cost $2000