Andrew Trask

@iamtrask

Helping people answer questions using data they cannot see Lead , SRS , PhD S. +, , Edu +

Oxford, UK
Vrijeme pridruživanja: studeni 2012.

Medijski sadržaj

  1. 29. sij

    IMO, this is the best work I've ever read on AI & Human Values Highly recommended read for everyone in the field It's a dense read - but push through. You'll be glad you did.

  2. 25. sij

    When I learn new idea, I must repeatedly learn it from multiple before it *clicks* If you're learning nets - add this to your list It discusses for: - images - audio - databases

  3. 24. sij

    is one of the most important techniques I don't often recommend PhD Thesis' - 's is exceptional. He's a brilliant writer! Check out this taxonomy / table of contents!!! 👇👇👇

  4. 17. sij

    After a year of dev, I am *extremely* excited to share this step-by-step tutorial Goal: to be the *easiest* intro to preserving, Deep Learning It's in I hope you enjoy it

  5. 16. sij

    If you've wondered - "Which optimizer should I use? ? ? ?" This blogpost by is the best explanation I've seen. It's a surprisingly easy read! Definitely a great / project!

  6. 15. sij

    The *easiest* way to learn Deep Learning is to build it from scratch! IMO, the same is true when learning a Deep Learning framework. In I show how to build a -like framework Here's the step-by-step code!!

  7. 11. sij

    King - Man + Woman = Queen How does this work? does an excellent job laying out the foundations. The code at the end is also a great project. Article:

  8. 3. sij

    For all you *aspiring* users! @KaiLashArul has written a *very* nice fast-track intro!

  9. 2. sij

    Want to know the future of insurance? It’s personal data modifying your coverage costs now offers variable pricing based on a driving tracker in your car Wouldn’t be surprised if medical insurance has this for exercise/lifestyle soon

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  10. 24. pro 2019.

    Attention is one of the most important breakthroughs in the history of Deep Learning. This is definitively the best explanation of it I've seen. For / folks - try building an attention mechanism from scratch!

  11. 17. pro 2019.

    This is the *most* extensive list of "From Scratch Machine Learning" I've ever seen. It's a *golden* resource if you learn like I do (by building things from scratch). Happy learning!

  12. 15. pro 2019.

    This series of is a VERY nice step-by-step intro to data science and machine learning. If you're just starting out - I recommend walking through these notebooks as a first primer Definitely a great project

  13. 12. pro 2019.

    Machine Learning in a company is 10% Data Science & 90% other challenges It's VERY hard. Everything in this guide is ON POINT, and it's stuff you won't learn in an ML book "Best Practices of ML Engineering" This is a lifesaver project

  14. 10. pro 2019.

    "A Beginner's Guide to the Mathematics of Neural Networks" ... a nice gem 🙂 And *very* cool illustrations !!!

  15. 3. pro 2019.

    This year I wrote a book teaching Deep Learning - it's goal is to be the easiest intro possible In the book, each lesson builds a neural component *from scratch* in Each *from scratch* toy code example is in the Github below.

  16. 28. stu 2019.

    For anyone who has ever thought - "Can I learn the math needed for Deep Learning all in one place (& maybe skip the other stuff)?" - this is quite a nice resource! "The Matrix Calculus You Need For Deep Learning" (Table of Contents 👇)

  17. 24. lis 2019.

    is only relevant for problems we have data for The *most* important problems are problems about people , , , etc. Want to solve them? Solve In my talk, I explain why:

  18. 10. ruj 2019.
    Odgovor korisniku/ci
  19. 23. kol 2019.

    How should you learn an algorithm? 1) Find an implementation 2) Strip it down to the absolute bare bones 3) Teach it line-by-line in a blog Here's mine - "A Neural Network in 11 LInes of Python" I already knew NNs - still worth it !!!

  20. 22. kol 2019.

    Probably the best GANs tutorial I've seen - written by The best tutorials strip away all the complexity into the simplest example possible. That's what I like about this ... "50 lines of PyTorch"

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