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. It's not necessary to completed all the material before applying, as long as it's complete by the start of this course, approximately 70 hours.
- 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)
- watching the first 2 online course videos before you applying, and completing all 7 lessons before starting the course, approximately 10 hours
- having completed the older version of the course (released last year) and having watched the first 4 lessons of the new course on the fastai library and PyTorch
|Dates||March 19 - April 30 (7 weeks)|
|Schedule||Mondays 6:30-9 p.m.|
|Location||101 Howard St.
San Francisco, CA
|Continuing Education Units||1.5|
Admissions for Deep Learning Part 2 starting March 19 is now closed. All applications received before March 2 are under review and will be contacted if a seat in the course becomes available.
Please contact firstname.lastname@example.org for more information.