Machine Learning (including Deep Learning and Reinforcement Learning) for Engineers — A Technical Primer (Part 2)

  1. Learn or review the required math (mostly calculus, statistics, and linear algebra);
  2. Take some introductory machine learning (ML) courses;
  3. Work on some of your own ML projects — learn by doing;
  4. Take advanced reinforcement learning (RL) and deep learning (DL) courses;
  5. Learn about AI ethics and governance so you’re a force for good, not evil and negligence, and;
  6. Start following leaders in the field and reading their papers.
This is how little code you need to get a top-notch model in 2020 (GPT-2) off the shelf to start playing.
  • Math for ML textbook: Solid intro textbook from Deisenroth et al. I would do a quick review here, and if you need more background, do the ICL course below.
  • Math for ML Coursera course: The fantastic best place to start from ICL. I also enjoyed Khan Academy’s visualizations of many linear algebra concepts (having last studied it in high school!).
  • Essential Math for Machine Learning: Python Edition: An EdX course from Microsoft.
  • Sergey Levine (Berkeley)
  • Jeff Dean
  • Francois Chollet
  • ML Review
  • Hardmaru (Google)
  • Anima Anandkumar — see her great talks here.
  • Fei-Fei Li
  • Michael Nielsen
  • Raohackr
  • Andrej Karpathy
  • Yann LeCun
  • Chris Olah
  • Ian Goodfellow
  • Chelsea Finn
  • OpenAI
  • Google Brain
  • DeepMind
  • KD Nuggets
  • Microsoft Research
  • Facebook AI Research (FAIR)

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