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You are an absolute genius when it comes to explaining stuff. Every single time I come across a new concept and want to get a good solid basic understanding, I turn to your channel first. Thank you so very much for doing this fantastic work.

Thank you very much

Thank you sir for this amazing video, it helped me last year in my NLP exam and now i'm refreshing my information's about transformers hoping to land an interview soon!

this was the best explanation i've ever seen in my life, (i'm not even a english native speaker, i'm brazilian lol)

Bedankt

Yo! StatQuest please make a video about pipeline concept in machine learning.(like this one clear and true)

We understood the calculation of weights and biases. But how would i know about the nodes...how do i understand the logic to connect all the inputs to activation function and to the output.?..and how many hidden layes we need? And no example for more than one hidden layer Could you please help me here

Can't we use correlation factor instead of Mutual information for continuous variable?

Thank you so much for a very easy-and-nice-to-walk-through video! I really enjoyed the explanations and well-prepared slides! Also you made it very nicely paced. Thank you :))

BAM!

awesome pca for dim reduction with vertical+horizontal+depth all in one 3-d rotates

The amount of effort for some of these animations, especially in these videos on Attention and Transformers in insane. Thank you!

@statquest - Hi Josh - Does it make sense to perform PCA on categorical variables.

BAM!

Hi all, with this seq2seq, can we apply for embedding a sentence, than using the output vector for semantic similarity ?

I am on vacation in Hawaii but I am watching your neural network video. This video is so entertaining to watch :) Tai

36:49

The explanation is so clean. I was clapping for him from my room. How can someone be so good at their job!

How these hidden states weights are determined, is it via backpropogation?

I feel like 15:15 deserved a triple bam, but maybe that's just me

man was womp womping before womp womping was "cool"

amazing thank you for the hard work!

Thank you very much

Where where I can apply the concept of principal component analysis 🙃

goated intro

Way way better than those taught in uni. Easily understood

BAM!!❤

Too much biology in the beginning itself!

I did not understand at 8:30 minutes. Please explain. Why 0.9 * 100 * 0.15 ? Not 100*0.15 or log2(1/(0.9**100))?

you did not show how the rest of the weights are updated? I need to understand how the derivative of the activation function affects the weight update

BAM BAM BAMMMM LOVED THE EXPLAINATION!!

I have a doubt..if the ROC plot is given, how to determine the threshold value used for classification for that confusion matrix?

I saw so many videos before, IDK , what the fcuk they were teaching. This explaination is awesome.

8:24 The main part of the video! Everyone must watch!!!

Amazing Video ✨

statquest is the goat ngl

StatQuest never disappoints

Thank you man. Appreciate such thorough but concise explanation.

Your videos are awesome! Makes things so much clearer! But I have a couple of questions: How do you handle the situation where a point has many identical points (ie. high-dim distance = 0)? How to calculate sigma_i? For example, if k = 10, but 7-8 of the neighbours are duplicates with Dij = 0, then sigma_i is undefined. Do I de-duplicate the data first and then add it back in at the end? And symmetrizing: Wij' = Wji' = Wij + Wji - Wij x Wji, yes? But aren't Wij and Wji only calculated for neighbours of i and j? What happens if Wij exists, but Wji does not? Do I add i as another neighbour of j's? (but then j would have more than k neighbours) I'm so confused.

You are very interactive person😉

Are the conditions at each leaf random? Or is there a criteria for selecting them?

So . In coding how can we calculate the derivative of loss function dont using math

you are great .. learning the concept with music awesome..

Heaven Of statistics!

Why don't we just equate d(sum of squared residuals)/d slope to 0, that point will give either the minima or maxima, if it is giving minima, then that will be the value of intercept, we need to take. Why can't we do this instead of checking using gradient descent?