Rezultati pretraživanja
  1. 31. svi 2019.
  2. Day 8 of . Coded the for as part of the scholarship. Continued playing around with while doing the zero to mastery course from on )

  3. 6. kol 2019.
  4. 21. lis 2018.

    uses (partial) derivatives to find optimal solutions to problems. It’s useful in optimization functions like because it helps us decide whether to increase or decrease our weights in order to maximize or minimize some metrics like loss.

  5. 22. tra 2018.
  6. 1. stu 2018.

    Implementation of gradient descent in python from scratch [hindi]

  7. 29. lis 2018.
  8. 4. stu 2019.

    If you want to know more about the next module keep an eye on this thread.

  9. 15. stu 2018.

    It’s really interesting to know that the point at which you start can lead you to a different minimum every time.

  10. 20. lis 2017.
  11. 4. velj
  12. 2. velj

    Fun : 2d in . It uses simple , so it performs oddly, but I'll later. I had a lot of fun and it feels great to be productive w/ hobbies again.

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  13. 2. velj

    -6. "We transform this problem into a multivariate problem that can provably be solved by a ." M.t. 'Wir formen das Problem um in ein multivariates Optimierungsproblem, das bewiesenermaßen durch ein gelöst werden>

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  14. 1. velj

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