But what is a Neural Network? | Deep learning, chapter 1


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Typo correction: At 14:45, the last index on the bias vector is n, when it’s supposed to in fact be a k. Thanks for the sharp eyes that caught that!

For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning:

There are two neat things about this book. First, it’s available for free, so consider joining me in making a donation Nielsen’s way if you get something out of it. And second, it’s centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning!

I also highly recommend Chris Olah’s blog:

For more videos, Welch Labs also has some great series on machine learning:

For those of you looking to go *even* deeper, check out the text “Deep Learning” by Goodfellow, Bengio, and Courville.

Also, the publication Distill is just utterly beautiful:

Lion photo by Kevin Pluck

Animations largely made using manim, a scrappy open source python library.

If you want to check it out, I feel compelled to warn you that it’s not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind.

Music by Vincent Rubinetti.
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If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then “add subtitles/cc”. I really appreciate those who do this, as it helps make the lessons accessible to more people.

3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you’re into that).

If you are new to this channel and want to see more, a good place to start is this playlist:

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  1. That sad part is that this is pretty much deep learning resarch. "So the one thing didnt work at some point, so they tried this other thing and for whatever reasons it happended to work. Now everybody is using this new thing."

  2. This might sound crazy, but I want to start referencing YouTube videos, such as this one, within academic writing. This would, however, require less informal language such as "heck" xD. Contact me if you might be interested in re-creating videos, such as this, in an academically acceptable format, then we could start the inevitable moving of the academic space towards videos.

  3. thank you verry much .
    i want to ask you about the programme that you use it for make your video with this animation effect ?
    thank you again

  4. Hi, I'm yukta . I want to ask question that how can I a make software that detects fake videos or deepfake videos…how to solve this problem …what should i have to study to make this software…. And I'm beginner 🙂

  5. 10:57 an alternative way of thinking about it is how certain that neuron is that that region of pixels has that specific shape based on the weights assigned to each pixel there – if it's more certain, the number will be higher. if it's less certain, the number will be lower or negative.

  6. Nice presentation, you are very good orator, I never heard you breathe, do you even need air 🙂 I was able to focus so well on the content and not the presentation.

  7. Как понять сколько нужно слоев чтобы матрица вещественных чисел 28х28 могла распознавать числа от 0 до 9?

    Как понять сколько нейронов должно быть в каждом слое, чтобы матрица действительных чисел 28х28 могла распознавать числа от 0 до 9?

    How to understand the minimum number of layers needed to the matrix of real numbers 28×28 could recognize numbers from 0 to 9?

    How to understand the minimum number of neurons should be in each layer so that the matrix of real numbers 28×28 could recognize numbers from 0 to 9?

  8. @3blue1Brown At 14:38 isn't matrix for bias supposed to be [k x 1]? instead of [n x 1]? I am not sure if I'm right but I think since there are k neurons at layer 1, the number of bias also should be k?

  9. So inside one neuron, do each weight for each neuron have different values? Or the same weight value for each input in one neuron?

  10. f that's the best subscribe request I ever seen at the end of a video: subscribe so the AI can take positive data, on a ai video, noice.

  11. There is an error at the matrix indexes at 16:00, the last column index at first row is n and at other rows it's k, should be consistent

  12. At 10:05 you say that adding the negative weights around to detect edges will increase the weighted sum, but surely as the activation value is between 0 and 1, it will decrease the weighted sum and thus affect which neurons are affected in the next layer?

  13. Typo: At 14:40 the vector of biases that is added should be [b0…bk] and not [b0…bn] because the size of the matrix that you get from the multiplication is [k+1] x 1 and not [n+1] x 1.

  14. I think there was a mistake at 14:42. The matrix for the bias should go from b0 to bk, not bn.

    Awesome video regardless.


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