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CodeEmporium

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Embeddings - EXPLAINED!

Embeddings - EXPLAINED!

Q-learning - Explained!

Q-learning - Explained!

ChatGPT: Zero to Hero

ChatGPT: Zero to Hero

Llama - EXPLAINED!

Llama - EXPLAINED!

Пікірлер

  • @tylersnard
    @tylersnardКүн бұрын

    Thank you for this. What are U, V, and W at 8:44?

  • @FirstNameLastName-fv4eu
    @FirstNameLastName-fv4eu2 күн бұрын

    Ajay you are starting a cult man!!! May God bless you.

  • @atamir8339
    @atamir83393 күн бұрын

    loved your explanation bro, earned yourself a sub

  • @scott7948
    @scott79483 күн бұрын

    Nice vid, but are you going to show us how to use a transformer to forecast into the future across multiple timesteps. I'm hanging out for that video. Cheers

  • @RakeshKambojVinayak
    @RakeshKambojVinayak4 күн бұрын

    Thnx

  • @hassanahmedkhan3834
    @hassanahmedkhan38345 күн бұрын

    Excellent Explanation, hats off.

  • @abhiksarkar9278
    @abhiksarkar92786 күн бұрын

    This is most appropriate and concise explanation of attention

  • @matankri
    @matankri6 күн бұрын

    Great content and well explained, but the excessive graphic visualizations like the moving characters is extremely distracting. Less is better.

  • @prodbreeze
    @prodbreeze7 күн бұрын

    B A B

  • @mahrym984
    @mahrym9847 күн бұрын

    Great summary thank you🙏🏾

  • @alirezasalehabadi1422
    @alirezasalehabadi14227 күн бұрын

    Thank you.

  • @prodbreeze
    @prodbreeze7 күн бұрын

    YOU HAVE MADE ME ACTALLY LIKE ML DL for the first time

  • @sammykmfmaths7468
    @sammykmfmaths74688 күн бұрын

    Please the video margin Is truncated 😢😢

  • @Sickkkkiddddd
    @Sickkkkiddddd8 күн бұрын

    How is information preserved with BN? If there was some pattern in the data pre-normalisation, wouldn't that be lost after batch norm?

  • @maga9852
    @maga98528 күн бұрын

    Hi @CodeEmpoium, Thank you very much for the video. It is very useful and well done. I just have one clarification. When you draw the Gaussian distribution corresponding to the point Xi (01:10-01:30), it seems that you make the peak of the curve coincide with the intersection of the interpolating line. Usually, it doesn’t happen this way; the peak is at the intersection of the vertical line passing through Xi and the interpolating line. As shown in this image: towardsdatascience.com/probabilistic-interpretation-of-linear-regression-clearly-explained-d3b9ba26823b Is that correct?

  • @maxsch.2367
    @maxsch.23678 күн бұрын

    absolute banger! well done

  • @telugumoviesfunnycuts5310
    @telugumoviesfunnycuts53109 күн бұрын

    Could not get anything from this. too complex

  • @verendrakharkwal3642
    @verendrakharkwal36429 күн бұрын

    How is it 20:1 for alexnet 👀

  • @jayktharwani9822
    @jayktharwani982210 күн бұрын

    great explanation.

  • @adelAKAdude
    @adelAKAdude10 күн бұрын

    great video thanks question ... in the third question ... how do sample subset of keys, queries "depending on importance"

  • @lucianofloripa123
    @lucianofloripa12310 күн бұрын

    Good explanation!

  • @vyax
    @vyax11 күн бұрын

    lmk if they say

  • @surajrao9729
    @surajrao972912 күн бұрын

    the best video so far

  • @sillystuff6247
    @sillystuff624712 күн бұрын

    you're a clear, calm explainer.

  • @darshh.poetry2193
    @darshh.poetry219312 күн бұрын

    Nice explanation

  • @myhofficiel4612
    @myhofficiel461213 күн бұрын

    well explained , you made it look really easy !

  • @algorithmo134
    @algorithmo13413 күн бұрын

    what book is that

  • @vishwasprabhu4047
    @vishwasprabhu404715 күн бұрын

    Vsauce plugin 😂

  • @poorinspirit8322
    @poorinspirit832215 күн бұрын

    1-B 2-ABCD 3-C are these answers correct?

  • @poorinspirit8322
    @poorinspirit832215 күн бұрын

    1-CD 2-D 3-D

  • @poorinspirit8322
    @poorinspirit832215 күн бұрын

    1 - B, 2 - B, 3 - C. Are these answers correct?

  • @BruceOnder
    @BruceOnder16 күн бұрын

    That's not Pong.

  • @poorinspirit8322
    @poorinspirit832216 күн бұрын

    1 - A 2 - ABC 3 - A Are these answers correct?

  • @thangarajr-qw6wy
    @thangarajr-qw6wy17 күн бұрын

    (1) supervised fine-tuning (SFT), (2) reward model (RM) training, and (3) reinforcement learning via proximal policy optimization (PPO) on this reward model explain me

  • @DevanshSagar-cy8kp
    @DevanshSagar-cy8kp17 күн бұрын

    Great work ❤

  • @thvk98
    @thvk9817 күн бұрын

    I have seen so many transformers videos but this one is outstanding, I also want to request you to make a video on vision transformers too❤

  • @marcdelabarreraibardalet4754
    @marcdelabarreraibardalet475417 күн бұрын

    Nice video, well explained. Question, why would I use one or the other? Are there advantages or disadvantages?

  • @user-jy5pu6bg5p
    @user-jy5pu6bg5p17 күн бұрын

    Is it your voice or what ai you use ?

  • @AymaneArfaoui
    @AymaneArfaoui18 күн бұрын

    what does x and y represent in the graph you use to show the cats and dog points ?

  • @Shrimant-ub4ul
    @Shrimant-ub4ul18 күн бұрын

    thank u so much

  • @ParthivShah
    @ParthivShah19 күн бұрын

    Really Appreciate Your Efforts. Love from Gujarat India.

  • @swethanandyala
    @swethanandyala19 күн бұрын

    The best explanations on transformers that i have seen!

  • @einsteinwallah2
    @einsteinwallah219 күн бұрын

    according to dr. c. k. raju calculus was stolen from india

  • @samson6707
    @samson670719 күн бұрын

    You are my new favorite channel

  • @aswinselva03
    @aswinselva0319 күн бұрын

    The video is informative and good. but stop saying quiz time in an annoying way

  • @benjaminlin8386
    @benjaminlin838620 күн бұрын

    Super clear, thanks!

  • @cphrase
    @cphrase21 күн бұрын

    1997

  • @VideoDarslar
    @VideoDarslar21 күн бұрын

    Easy

  • @Piku.aka.Priyanka
    @Piku.aka.Priyanka21 күн бұрын

    1. either of MSE or MAE as both are used as loss functions for regression. 2.A,B,C 3.A are these answers correct?

  • @Eysh2009
    @Eysh200923 күн бұрын

    Valeu!

  • @CodeEmporium
    @CodeEmporium22 күн бұрын

    Thanks so much for the donation! Glad you liked this content!