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 TH-cam channel for Data Science. Keep making content like this, Thank you.
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
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
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?
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
the best teacher ever, You're teaching in a very simple and easily understandable. thanks alot
Perfect teacher and ur lecture matches all inteligence levels
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.
Thanks Naveen. Pls share with friends as well.
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.
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
Best explanation
Nice explanation !! Keep it up !!
Very detailed explanation. Thank you so much Guruji!
Heads off to you again, m already your Fan....!
7 inputs * 3 output = 21 + 3 outpus bias = 24
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
nice session
Understanding the Natural Languaging sir.
Sir You are too good 🎉🎉🎉
please upload videos for image classification CNN also .very much needed.
You are awesome man. Loved this video.
Glad you enjoyed it Santosh.
excellent explanation
Good explanation
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 !
Waiting for next 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
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.
Ur r always 😊
Thanks Umesh. hope you are doing good