Prerequisites for participants
The Course isn't an introduction to programming - that can be found elsewhere.
Rather than specific credentials, we're looking for people who are enthusiastic about becoming Deep Learning Developers,
and are ready to work hard to make themselves 'work-ready' within 8-10 weeks of nights & weekends.
Participants should be able to demonstrate the following experience and skills during their application :
- Minimum 1 year of programming experience (preferably in Python)
- Basic statistics knowledge (mean, variance, standard deviation, etc.)
- Have covered (at some point) vectors/matrices, and basic calculus
How the program is organised
The course lasts 8 weeks, with in-person lectures (2 instructors, both with hands-on experience) and
project-based tuition once per week. In each 3 hour session, participants should expect 60-90 minutes
of lecture material, with the remainder being collaborative tuition on the various projects. In addition to
the 3 hours of weekly time in the classroom, participants should expect to watch additional materials
(video lectures, for instance) and do project work – totalling an extra 3-5 hours per week.
Based on experience, we may insert one or more week-long catch-up periods, so that people
can make sure the get enough time on their project-work to make it a success. Of course, if
things work first time, then that's great. But we believe that it's better to learn thoroughly
than in a mad rush to 'get it done in time'.
What participants will learn and accomplish
Participants will learn the basic theory behind deep learning techniques, and (more importantly) the
practical steps required to implement their own models. The following areas will be included :
- Machine learning (recommender systems); CNNs (vision), RNNs (NLP), unsupervised learning;
- Deploying TensorFlow models locally; to the cloud; and to mobile devices;
- Project work (1-2 weeks each) : 3 'coursework projects' and 2 individual self-motivated projects.
What your employer / potential employer can expect
People who have successfully completed the Course will have a solid grounding in the foundations of
Deep Learning, as well as having independently built projects of their own. They should be able to
appreciate the engineering challenges that can be tackled using Deep Learning, and be able to hit the ground running.