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:

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

  1. 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…??

  2. 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

  3. 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?

  4. 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 ?

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