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This is one of the best. Simply superb.
The simplest and precise explanation ever seen! thank you, sir
Thanks Kaushik
Excellent teaching
Hello professor .. appreciate your efforts.
you has 32x32 in slideshow, but in coding, that becomes 28x28 as input, that gets us confused with the 28x28 output in the slide
Going from the input image to the first hidden layer, How do we generate 6 feature maps? Do we apply 6 different filters to the input image to get these 6 feature maps?
nice explanation but it would be better if you had explained about why we got 16 feature maps
Lively and lovely explanations!
Clear explanation sir.Can you please please tell me how to create a mat file in Python
Nice explanation. Awesome tutorial! Thanks for the efforts and hardwork.Could you provide the link of the sample code?
Thanks
In the git .. @shriramkv
@@ShriramVasudevan Thanks sir.
give me
i am confused, the first Dense layer takes a 1-D vector as input, so it outputs also a 1-D vector, why we need to flatten this output?
sir i got error in history=model.fit() as saying for using self
its very nicely explained and with brevity. Why dont you please share the code for reference and trying out in the description plz
Thanks and its in my Git @shriramkv
Thanks for this informative video, I'm working of detection the plants image. Can I apply this code for classification and detection?
Yes. With some tuning.
@@ShriramVasudevan Can I contact you?
IN WHICH COMPILER WE HAVE TO RUN THE CODECAN YOU P;LEASE SAHRE THE CODE IN DESCRIPTION
you should also explain how we got 28,28 size and 5,5 and math behind it
but , how can we finalize that 1 as DOG . 0 as CAT
IS it possible to use extracted features as input to LeNet
Yes
sir, are we fixing 120 feature map and 84 feature map and softmax as 10 or is their any mathematical formula for this?
hey professor ...thanks for the simple explanation. It would be great if you provide the code link in the description.
Sure. Thanks brother
This is fantastic. Thank you
Thanks sourav
SIR i have understood 6 feature map, but how we get 16 feature map in step 3 i.e 16x10x10
Let me clarify shortly..
Sir can we change the value of feature maps or its already defined by default.
We can.
where to get dataset and code
Good Explanation sir... Can you please share your code?
Thanks and check in my git..shriramkv
Nice session
Thank you
thanks
You're welcome!
This is one of the best. Simply superb.
The simplest and precise explanation ever seen! thank you, sir
Thanks Kaushik
Excellent teaching
Hello professor .. appreciate your efforts.
you has 32x32 in slideshow, but in coding, that becomes 28x28 as input, that gets us confused with the 28x28 output in the slide
Going from the input image to the first hidden layer, How do we generate 6 feature maps? Do we apply 6 different filters to the input image to get these 6 feature maps?
nice explanation but it would be better if you had explained about why we got 16 feature maps
Lively and lovely explanations!
Clear explanation sir.
Can you please please tell me how to create a mat file in Python
Nice explanation. Awesome tutorial! Thanks for the efforts and hardwork.
Could you provide the link of the sample code?
Thanks
In the git .. @shriramkv
@@ShriramVasudevan Thanks sir.
give me
i am confused, the first Dense layer takes a 1-D vector as input, so it outputs also a 1-D vector, why we need to flatten this output?
sir i got error in history=model.fit() as saying for using self
its very nicely explained and with brevity. Why dont you please share the code for reference and trying out in the description plz
Thanks and its in my Git @shriramkv
Thanks for this informative video, I'm working of detection the plants image. Can I apply this code for classification and detection?
Yes. With some tuning.
@@ShriramVasudevan Can I contact you?
IN WHICH COMPILER WE HAVE TO RUN THE CODE
CAN YOU P;LEASE SAHRE THE CODE IN DESCRIPTION
you should also explain how we got 28,28 size and 5,5 and math behind it
but , how can we finalize that 1 as DOG . 0 as CAT
IS it possible to use extracted features as input to LeNet
Yes
sir, are we fixing 120 feature map and 84 feature map and softmax as 10 or is their any mathematical formula for this?
hey professor ...thanks for the simple explanation.
It would be great if you provide the code link in the description.
Sure. Thanks brother
This is fantastic. Thank you
Thanks sourav
SIR i have understood 6 feature map, but how we get 16 feature map in step 3 i.e 16x10x10
Let me clarify shortly..
Sir can we change the value of feature maps or its already defined by default.
We can.
where to get dataset and code
Good Explanation sir... Can you please share your code?
Thanks and check in my git..shriramkv
Nice session
Thank you
thanks
You're welcome!