The intro song feels like Phoebe Buffay used to sing from 'Friends' 😂 BTW Nice Explanation ❤

2024 PHD Student 10000000 BAM

puop poopup pooh

Very Beautifully Explained as Always. It takes a great amount of intuitive understanding and talent to explain a relatively tougher topic in such an easy way. I just had some doubts - 1. In case of context aware embeddings of a Sentence of a Doc are the individual Embeddings of the tokens averaged. Does this have something to do with the CLS token ? 2. Like a Variational Autoencoder helps in understanding the intricate patterns of images and then creates its own latent space , can BERT (or any similar model) do that for Vision task (or are they only suitable for NLP Tasks) 3. Are Knowledge Graphs made using BERT ? Any help on these will be appreciated . Thank You again for the Awesome Explanation

hi smal mistake: 1- 3/8 should 5/8 at 10:31 rest all good, love your videos!

AAAAASDF

Thank you sir. I just suffered through a Math Stats class at a well-regarded graduate program that never explained MLEs as well as this short video.

Bruh is so confusing

If you could please make a video on assumptions of linear regression, that would be helpful.

BAM!!! Thanks mate

thisss was very fun and enjoyable to watch, thank you !!!!! :))

1:29 this decision tree can simplified to either half of the silly songs decision and it would generate the exact same results

14:30 a little let down with no BAM.

the psst kill me every time :)

You are reminded me of my teacher. You are so cool, dear StatQuest🤩

I still don't get what P-value is and why we need that?

my teacher keeps flying to new york and doesn't teach us crap about this so thank you for this pookie <3

BAMMM indeeed

To be consistent with you distinguishing probability from likelihood; you should say probability AND likelihood instead of OR.

I was thinking about creating embeddings for a domain specific task to use it later in helping processing Research Papers. The task that I had in my mind was that I want to interpret Research Papers in the Chemistry Domain Specifically. For that I need word embeddings specifically trained on this type of Data. The problems that I am encountering are that - 1) How to get this Data in bulk 2) How to train the embeddings. Should I use an AnutoEncoder to train the Embeddings so that they have contextual and semantic understanding or should I use Transformers The trained Embeddings can later be used for more task later like they will help in training other models which will help in parsing lab submissions, other research work etc Please if anyone can help it would be great

Tremendous explanation of how classification tree works!!!

Reason behind why we would like a lower pvalue for our R-squared : Null hypothesis - The model explains no variance in the dependent variable (R squared = 0, the predictors have no effect) Alternate hypothesis - the model explains some variance ( R squared is greater than 0)

Nice tutorial and very well explained as always @statquest. Have one question though. The screen at 16:22 shows x -1.30 and x 2.28on the connections leaving the hidden layer, but in reality they are changing y value. Is there a reason why it is mentioned as x? Also the results from two connections are summed to get the final result. In reality does this sum happen in another node? Does it have a special name like combiner or aggregator?

in conclusion.... shame on shannon!!

Clear as mud - and I've got a degree in statistics.

Hi! Love your videos! I have one question, in this video, you started with plotting the variables on samples as the axes, but on your newer video about PCA, you started the other way around. Why are these two videos different? Or am I getting it wrong?

THANK YOU

Thank for the KNOWLEDGE

LOL Good video, but usually if you see a "Dear friend" in the email it is spam. And if you see "lunch" it is usually a normal email despite how many times the word "money" was written. It could be a discussion of a budget for a trip and where we should have lunch together. But yeah, that is why it is naive, I guess.

I don´t know but this Bam pushed me out of my fking chair. It´s so cool to understand how it works. Thank you so much!!

wow, such an amazing explanation!!!

"Take me to your leader" is a classic.

Hey bro, we all know that you have very much knowledge about Machine learning Algorithms, you are like a gentleman but don't use very smart word and not sing between the video's, if you want to become a singer then go for it , but don't make a video in funny way like Singing or murmuring

Thanks you saved my life!

Infinity BAM!

You're wondeeful❤

I didn't understand why are the data sets perpendicular to each other. Can anyone please explain😅

Hello Josh sir, Can you please make a video on SUPPORT VECTOR REGRESSION

5:33 can anyone explain this part more clearly

The way you introduced SUPPORT VECTOR MACHINES..!!!☀ BAM!!!

GitHub Copilot Chat recommended this to me, suggesting it was the next URL I needed in my decidedly not clustering related code, lol

I couldn't know how to say both words in my language, because for Google translate, probability and likelihood both translate to the SAME word. Cool.

Thank you!

god bless your double bam soul josh, thank you

penta BAMI!!.

i really wanted to see how you would handle a target categorical variable with more than 2 categories....

This is easy to understand and sooooo fun to watch. I literally laughed out loud at the "BAM"s, hahaaaa you're so funny. people around me might be thinking I'm watching some random Mr Beast videos

Thank you for the class Josh! Why not not use dpred/dw1 directly in the dssr/dw1 calculaction?

Greate work. You might not know how good you are . Let me tell you. You are a gifted teacher.

statquest is best