Відео

speak louder pleaase

You've inspired me to start my own channel. This is the best video I've seen all year! Thank you for your contributions and for continuing to inspire us!

Amazing video, thank you so much

Amazing video...

Thank you Mr. Karpathy. I am in love with your teaching. Given how accomplished and experienced you are, you are still teaching with such grace. I dream about sitting in one of your classes and learning from you, and I hope to meet you one day. May you be blessed with good health. Lots of love and respect.

You are incredible generous. Thanks immensily for sharing your knowledge.

I tried to apply the reverse curse but Chatgpt is giving the right answer in both ways. Can anyone tell me why so?

This tutorial was perfect, so helpful! thank you so much for sharing!!!

Thank Andrej, Great video! You it very easy to understand a complex subject

Such a superstar!!!!!!

Thank you so much Andrej!

I love how you take nn, and explain to us, not by already built in function in pytorch, but by how things works, then giving us what the equivelent lf it in pytorch

GOD

This is the best back propagation explanation I ever got. Congratulations! This is great!

In this lecture at time arount 18.50 Andrej says that you get white when the statement is true and black if the statement is false. This is actually the opposite. The dead neuron problem occurs when you get completely black neurons under the imshow. In those cases the gradient does not change for the upstream parameters. Does anybody agree+

The legend that introduced me to DL all those years ago continues to awe us. Bravo

Hi! Can you make video explanation about new Kolmogorov Arnold Network (KAN) ? It's seems to be a new revolution in NN 🤔

This was very effective, knowledgeable and overwhelming at the same time 😂

Brilliant video!

extremely simple and interesting! Thank you!

does anyone else hear that crazy loud ringing noise?

thank-you! audio is very low though

My tiny brain just blew up!

hi everyone if i have losses like this (train loss 0.6417 val loss 6.7432) but an output not bad, is it normal?))

Hi Andrej, thanks a ton for the free education, I am enjoying learning throught the lessions! A quick observation, the output of the explicit counting method and the nn method seem different in the end: counting table sampling -> nn sampling: koneraisah. -> kondlaisah. andhumizarie. -> anchthizarie. can you comment? my intuitions is telling me that the nn is just an approximation of the countering table, never exact. Not sure if this is the right way of reasoning. Thanks!

Thank you so much Mr Andrey kaparthey, I watched and practice a pytorch course of like 52 hours and it was awesome, but after watching your video, it's seems that I was more of like learning how to build a neural network, more then how neural network works. With your video I know exactly how it works, and iam planning to watch all of this playlist, and see all of almost all your blog posts ❤ thank you and have a nice day.

Brilliant

Thank you so much!!! For democratizing education and this technology for all of us! AMAZING! Much much love!

❤ > It's (a) pleasure

fine tuning next please

Is it possible to just calculate the gradients once and then you know them? Not resetting and recalculating. What am I missing?

Thanks!! I have never understood better how a neural network works!

One of the best videos on under the hood look in LLMs. Love the clarity and patience Andrej imparts considering he’s such a legend in AI.

Was i the only one invested with the leaf story😭

Hi Not sure what happens, but I can't join discord, it says "unable to accept invite". I have issue to get the "m" from multinormial distribution, I got 3 instead 13 if I use num_samples=1. if I use num_samples>1, I got 13. but I got "mi." if num_samples=2. people on line suggest to use touch 1.13.1, I dont have this old version on my macOs.

Wow!!! This is extremely helpful and well-made. Thank you so much!

This guy has become my favorite tutor .

I love this learning resource and the simplicity of the tutorial style. Thanks at Andrej KArpathy.

In the case of a simple Bigram model @32:38 we are sampling only one character, however, while calculating loss we consider the character with the highest probability. The character sampled is unlikely to be the same to the character with the highest probability in the row unless we sample a large number of characters from the multinomial distribution. So, my question is, does the loss function reflect the correct loss? Can anyone help me understand this.

Thanks I really need this level of details to understand what’s going on. ❤

1:25:23 Why is the last layer made "less confident like we saw" and where did we see this?

This is the best video ever about Intro to LLM.

How were you able to run the loop without adding a requires_grad command in the "implementing the training loop, overfitting one batch" section of the video? For me it only worked when I changed the lines to: g = torch.Generator().manual_seed(2147483647) # For Reproducibility C = torch.randn((27, 2), generator=g, requires_grad=True) W1 = torch.randn((6, 100), generator=g, requires_grad=True) b1 = torch.randn(100, generator=g, requires_grad=True) W2 = torch.randn((100, 27), generator=g, requires_grad=True) b2 = torch.randn(27, generator=g, requires_grad=True) parameters = [C, W1, b1, W2, b2]

This is the most amazing video on neural network mathematics knowledge I've ever seen; thank you very much, Andrej!

Something you didn't explain - 51:30 - if we want L to go up we simply need to increase the variables in the direction of the gradient? How come it is so if some gradients are negative?

This is the single best explanation of backpropagation in code that I've seen so far. Thanks Andrej.

Thanks a lot for the insight. and demonstration. I really look forward more videos from you, Andrej!

just a random fun fact, with gen = torch.Generator().manual_seed(2147483647), the bigram generated name i got was `c e x z e .`, amazing.

Thanks a ton for this Andrej! Explained and presented in such simple and relatable terms. Gives confidence to get into the weeds now.

The best ML tutorial video I have watched this year. I really like detailed example, and how these difficult concepts are explained in a simple manner. What a treat for me to watch and learn!