8-Week Deep Learning Developer Course (2017)


    • July - September (catch-up in August)
    • Weekly in-person 3-hour sessions which include :
      • Instruction by Martin Andrews and Sam Witteveen
      • Project work is a core element :
          3 structured/coursework 1 week each
          2 self-motivated projects, each 2 weeks
    • Expect to work hard...

    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.