NLP history up to RNN| Natural language processing in artificial intelligence | NLP course

[@rajeevmishra7486]

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

[@nanyongaaziidah7245]

the best teacher ever, You're teaching in a very simple and easily understandable. thanks alot

[@nagasudha6928]

Perfect teacher and ur lecture matches all inteligence levels

[@naveenmurugan2048]

You are teaching in a very simple and easily understandable, you became my favorite TH-cam channel for Data Science. Keep making content like this, Thank you.

[@UnfoldDataScience]

Thanks Naveen. Pls share with friends as well.

[@mayanklohani19]

Following u from last 6 months. You are a gem ❤ Cracked 3 interviews following ur videos.

[@UnfoldDataScience]

Awesome! Thank you Mayank. Good luck and pls share with friends.

[@dharmaraj7411]

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

[@knowfact2]

Best explanation

[@piusranjan]

Nice explanation !! Keep it up !!

[@vijayaselva7851]

Very detailed explanation. Thank you so much Guruji!

[@shashankbarai1809]

Heads off to you again, m already your Fan....!

[@samadhanpawar6554]

7 inputs * 3 output = 21 + 3 outpus bias = 24

[@muhammedthayyib9202]

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)

[@sarans3185]

how number of input layer is 7 ,how it is 8

[@AISynthetic]

@@sarans3185 not 8 *3 = 24 but the correct oen is 7*3 = 21 + 3 ( biases) = 24

[@chandanghosh1389]

nice session

[@salomishiny6997]

Understanding the Natural Languaging sir.

[@vaishalibande7140]

Sir You are too good 🎉🎉🎉

[@mukulkulshreshtha1413]

please upload videos for image classification CNN also .very much needed.

[@SantoshKumar-jw8hw]

You are awesome man. Loved this video.

[@UnfoldDataScience]

Glad you enjoyed it Santosh.

[@asgharchanna52]

excellent explanation

[@techsupport8674]

Good explanation

[@ksaha6387]

Exactly the kind of video I was searching for past 2 weeks.. He knows his market and TG 😂😂

[@UnfoldDataScience]

Thanks Saha,hope u liked it. If yes pls share with friends.

[@ksaha6387]

@@UnfoldDataScience Yes.. You deserve better subscriber numbers !

[@muhammedthayyib9202]

Waiting for next video

[@c.nbhaskar4718]

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

[@UnfoldDataScience]

Thanks Bhaskar . Yes one by one we will go.

[@manjeerag868]

​@@UnfoldDataScienceYes please!
Need a detailed video on transformers, bert etc
Thanq

[@tejkiran1836]

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?

[@UnfoldDataScience]

Hi Tej, pls check tomorrow you will find video.

[@umeshtiwari800]

Ur r always 😊

[@UnfoldDataScience]

Thanks Umesh. hope you are doing good