Encode decoder seq 2 seq architecture| encoder decoder model | encoder decoder neural network

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  • เผยแพร่เมื่อ 11 ก.ย. 2024
  • Encode decoder seq 2 seq architecture| encoder-decoder model | encoder-decoder neural network
    #machinelearning #chatgpt #datascience #ai
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    Topics for the video:
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ความคิดเห็น • 29

  • @user-ug9fb7mm3e
    @user-ug9fb7mm3e 3 หลายเดือนก่อน +2

    The best explanation I have ever seen, thank you!

  • @AbhishekVerma-kj9hd
    @AbhishekVerma-kj9hd 8 หลายเดือนก่อน

    Never have I ever seen this an easy explanation like this, you earned a subscriber.

    • @UnfoldDataScience
      @UnfoldDataScience  8 หลายเดือนก่อน

      Thanks Abhishek. Wish you a very happy new year.

  • @charleskangai4618
    @charleskangai4618 6 หลายเดือนก่อน +1

    Excellent, brilliant explanation of transformers!

  • @maruthiprasad8184
    @maruthiprasad8184 8 หลายเดือนก่อน +1

    Thank you so much for the simple and clear explanation

  • @vasutke1187
    @vasutke1187 ปีที่แล้ว

    Latest & Greatest @ finest which is understable with Professional style videos. Thanks so much.

  • @sounishnath513
    @sounishnath513 ปีที่แล้ว

    You are doing really good job. Keep it on!.
    Since you are breaking the concepts into very granular blocks, is needed really helpful anyone who starts with and revise.

  • @pythondemon
    @pythondemon 5 หลายเดือนก่อน

    Wow, you made a complex subject extremely simple. Thank you so much 🔥

  • @bhavikdudhrejiya852
    @bhavikdudhrejiya852 10 หลายเดือนก่อน

    What an amazing explanation sir, I really appreciate your effort to simplify this concept.

  • @chithrasrinivasan9904
    @chithrasrinivasan9904 2 วันที่ผ่านมา

    Good Explanation and understandable to layman. Can you also explain what will the predicted probability/output vector for EOS?

  • @MayankArya-jp7dq
    @MayankArya-jp7dq 2 หลายเดือนก่อน

    Nice way of teaching :)

  • @KhairulMia-tr2jv
    @KhairulMia-tr2jv 13 วันที่ผ่านมา

    so good continue on LLM,Langchain,RAG,GEMNI

    • @UnfoldDataScience
      @UnfoldDataScience  13 วันที่ผ่านมา

      These videos are already there - Kindly search "Unfold data science + topic name" on youtube

  • @AnkitGupta-rj4yy
    @AnkitGupta-rj4yy 11 หลายเดือนก่อน

    Waiting for the next one please continue the series ❤ 😊

  • @rakeshkumarsharma2250
    @rakeshkumarsharma2250 4 หลายเดือนก่อน

    awesome videos , i want to learn about attention and transformers

    • @princekhunt1
      @princekhunt1 2 หลายเดือนก่อน

      CampusX YT Channel

  • @sg28011
    @sg28011 6 หลายเดือนก่อน

    Sir did not understand the START word and also what happens at the output of morneg output?

  • @jallaaswini9253
    @jallaaswini9253 8 หลายเดือนก่อน

    Can you do complete series on Generative AI

  • @roversncoders
    @roversncoders 5 หลายเดือนก่อน

    Do you have videos on Self attention, transformers?

  • @tvssarkar9568
    @tvssarkar9568 ปีที่แล้ว

    Sir make video on current market situation for data science domain please request

  • @mahipatil369
    @mahipatil369 11 หลายเดือนก่อน

    hello sir ,, r u going to continue to make sessions on attention , transformers and bert ?? when u r uploading next video?? plz rply

    • @UnfoldDataScience
      @UnfoldDataScience  11 หลายเดือนก่อน

      Yes, definitely. I am occupied with few things so not able to take out time, but it will come

  • @thandravishwaknarayana21bc59
    @thandravishwaknarayana21bc59 หลายเดือนก่อน

    good morning

  • @bhupathivineethkumar6040
    @bhupathivineethkumar6040 11 หลายเดือนก่อน

    Can u continue the play list

  • @tejkiran1836
    @tejkiran1836 ปีที่แล้ว

    Hi aman those links are character to character encoding.. Can we also do word to word encoding right? Which is better char2char Or word2word....
    Can you also explain how words get predict in the testing phase.... Bcz we don't have target words right? We only pass context vector of the inputs sentence to the decoder..
    Thanks

  • @yp1603
    @yp1603 7 หลายเดือนก่อน

    Hi brother can yu please continue this playlist by uploading transformer,bert and so on