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.
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 |
Contact Info
The Data Institute
Mon-Fri, 9 a.m. - 5 p.m.