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.
- 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
|Dates||March 18 - April 30 (7 weeks)|
Mon Mar 18
Mon Mar 25
Wed Apr 3
Wed Apr 10
Wed Apr 17
Tue Apr 23 (w/Chris Lattner)
Tue Apr 30 (w/Chris Lattner)
|Location||101 Howard St.
San Francisco, CA
|Continuing Education Units||1.5|