NLP history up to RNN| Natural language processing in artificial intelligence | NLP course
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- Опубліковано 19 лип 2024
- NLP history up to RNN| Natural language processing in artificial intelligence | NLP course
#nlp #deeplearning #machinelearning
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My name is Aman and I am a Data Scientist.
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the best teacher ever, You're teaching in a very simple and easily understandable. thanks alot
Very detailed explanation. Thank you so much Guruji!
Perfect teacher and ur lecture matches all inteligence levels
you got your trainable params as 24 in this way :
trainable params = (vocab words * dimensions)
in your case, vocab words = (n + 1 , n = no. of unique words)
dimensions = 3.
so, you have 8 * 3 = 24 trainable params
You are teaching in a very simple and easily understandable, you became my favorite UA-cam channel for Data Science. Keep making content like this, Thank you.
Thanks Naveen. Pls share with friends as well.
Excellent and easily understandable explanations. waiting for your videos on attention mechanism and LLM. Requesting you to please make a video on how to utilize pretrained models on LLM
Heads off to you again, m already your Fan....!
excellent explanation
You are awesome man. Loved this video.
Glad you enjoyed it Santosh.
Following u from last 6 months. You are a gem ❤ Cracked 3 interviews following ur videos.
Awesome! Thank you Mayank. Good luck and pls share with friends.
Sir You are too good 🎉🎉🎉
nice session
Good explanation
Understanding the Natural Languaging sir.
7 inputs * 3 output = 21 + 3 outpus bias = 24
Waiting for next video
please upload videos for image classification CNN also .very much needed.
Exactly the kind of video I was searching for past 2 weeks.. He knows his market and TG 😂😂
Thanks Saha,hope u liked it. If yes pls share with friends.
@@UnfoldDataScience Yes.. You deserve better subscriber numbers !
Hi aman thanks for the video.. I saw the video unfold RNN while I was in office.. I thought i can watch once i back home..Is that deleted? Will you upload it later?
Hi Tej, pls check tomorrow you will find video.
Hi aman , i am waiting for a video on Attention mechanism and transformers in an easily understandable way .I have searched many channels , but could not find it
Thanks Bhaskar . Yes one by one we will go.
@@UnfoldDataScienceYes please!
Need a detailed video on transformers, bert etc
Thanq
Ur r always 😊
Thanks Umesh. hope you are doing good
Number of units in the input layer is 8
Number of units in the output layer is 3
Total number of trainable parameters in 8*3=24 (Since it is a Dense)
how number of input layer is 7 ,how it is 8
@@sarans3185 not 8 *3 = 24 but the correct oen is 7*3 = 21 + 3 ( biases) = 24