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How do we learn? In this video, I’ll discuss our brain’s biological neural network, then we’ll talk about how an artificial neural network works. We’ll create our own single layer feedforward network in Python, demo it, and analyze the implications of our results. This is the 2nd weekly video in my intro to deep learning series (Udacity nanodegree)

The coding challenge for this video:

Ludo’s winning code:

Amanullah’s runner up code:

Please subscribe! And like. And comment. That’s what keeps me going.

More learning resources:

The guy at the beginning is my Jeet Kune Do instructor (Sifu Tim). Send him an email at [email protected] if you thought he was cool in the video. He would absolutely love it. Special thanks Catherine Olsson of OpenAI for being the hook to my backpropagation rap.

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لینک دانلود

45 پاسخ به “How to Make a Neural Network – Intro to Deep Learning #2”

  1. I am looking another person. I am not suitable for this. I am just wasting my time. It is not for beginners.

  2. Thank you for the video!!

  5. Hey, i am facing a problem in this example,

    I followed the video step by step,

    but the program only able to predict values for specified input set by you only…

    as soon as i tried to change the input and output data (tried it for 3 bit XOR logic)…same program gives trash output with increase in error..!!..

    can anyone please help me…??

  7. Sorry. I not understand . Note: 4:41. why you have '2' * random.random(3,1) – "1'

    where '2' and '-1' you can help me explain it ? thank you.

  8. can someone explain why we are using "-1" and "2 *" in this code
    self.synaptic_weights = 2 * random.random((3, 1)) – 1

  9. Holy moly is this python 2 I am a noob but looks like that to me because the print statement does not have those parenthesis around it… if so, can you please upload the code for this lesson to your GitHub for the python 3 version because most newbies are encouraged to learn python 3

  11. TFW you know maths but you don't know coding so you understand what's happening but on a much deeper level you have no idea what's going on.

  13. You're a life changer SIRAJ

  16. how u make good songs?
    wht d u think there will be ai popstar in future?

  17. lol famo just rapped lol

  21. So, is this essentially just a brute force, trial and error, attempt at solving multivariable equations? Are we just taking advantage of compute speed and plugging numbers into unknown variables until we assume it has the right formula due to apparent success rate?

  23. Can someone explain, how did we calculated the adjustment?

  24. sounds like MO BOUNCE BOUNCE bounce bounce bounceeeeeee

  25. Line 13, why multiply by 2?

  29. Does me think you should have more than a few million followers, makes me a geek?!

  31. This is an excellent video!

  34. Hi ! . 4:15 . There's a fast change. Where do u define .think() ?. Why do u transpose training_set_outputs ? .

  35. Siraj Raval please make video about neural network using JAVASCRIPT or C++.plz,plz,pzl,plz,plz,plz,plz,plz,plz,plz,plz,plz,plz,plz

  36. You are always great! Now I have a question! Is Neural Net is only for binary inputs?

  37. Brother, please add hindi subtitles on the videos.

  39. “李察斯” ????
    Sorry I've been staring at this kong fu guy's tattoo for too long. I guess his name is Richards XD

  40. haha you copied that code from internet say the truth:PP

  41. Those who have problem in his speed please reduce the video speed , watch this video on .75 speed .you will not face any problem then.Any way Siraj is awesome.

  42. Is that one layer neural network related to a logistic regression ? Except the estimation part where in this case you are descending that gradient (and having an accident) ? And also except the feed back thing ?

  43. Man youre going way fast, is this same as the nanodegree in udacity if it is I'm not gonna waste my money on it

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