A friendly introduction to Convolutional Neural Networks and Image Recognition

Very crystal clear explanation, helped me a lot to remove any confusion while doing masters!! Much thanks!

Great and clear explanations. Thanks for all your great videos, they really help me to understand..

this is by far the best Barebone illustration that I’ve seen and easy to understand the concept of CNN, bravo!

Can you elaborate how did you create the filters in the last layer

really really amazing I couldn't imagine someone can explain a complex concept so simply and also completely

Can you please explain how to create the filters for the last layer please..Since we have a Feature maps..how do we do regression on feature maps to create filters..

Thank you, Sir.

in 2023 I asked an AI to recommend me a concise and digestable video on CNNs... and it couldn't have been more on point

Omg how did I stumble upon such a well explained lecture!

I would appreciate if you can also do a video on LSTM

You have the ability to explain in very simple terms. I enjoy seeing the video as i understand the basics easily. You should also do videos on statistics, data transformations

wonderful sir.. could you please share the ppt

Great explanation.

Wow! You explain 1000000 times better than any professor at my university :D Thank you!

Lovely!!

thank you for your efforts

Ultimate intuitive series. Thanks for the Knowledge sharing..I think I am able to understand it now..Also parallelly I am learning the math of it through the other courses..so able to connect the dots

easy and funny haha :D

can you please suggest good book to get grip on machine learning?

most friendly (till now) introduction to cnn indeed :D
thx!

Simple as it is, this is truly a masterpiece. You have made it so straightforward and intuitive. Thank you.

This is amazing!

Not a bad explanation, perhaps a little unnecessarily complicated in a couple areas, but, and I have this issue with other cnn videos, if learning (training) takes place in the fully connected layers, how do the filters get figured out? I can see how it works on the simple example with diagonals, but if you have several layers of conv. and pooling, how does the final full connected layer propagate all the way back to the first layer to figure out, say, the features of a face? No one has properly explained this. Thank you.

Easily the best channel out there. How do you even think like this?!

Thanks a lot for your time and effort! Jajakallah!

that was an awesome class. thanks for your time. big shout out from brazil

One of the best if not the best video explaining CNN's online! Bravo!

Thank you very much.

Thanks

On point as always, thank you Luis!

Great video about CNN. Can you also explain about ANFIS model

Amazing , you have made it very very simple explanation , thank you so much

Thanks man

This is excellent!

Thank you so much, this is the best intro I have ever listened to CNN. Simple but not simplistic, clear. Three minor suggestions I can give you for a possible 2.0 version are:
- to expand a little the gradient descent. You calculated with patience the result of all filters but the gradient descent, in turn, is kind of evasive;
- to complete the mapping to a neural network with weights and biases or at least give the idea how to;
- explain the determination of the threshold (in the example you correctly put it to 3 but the determination is not evident).
Conclusion: one of the best tutorial I randomly stumbled into. My sincere compliments.

My Professor should watch this video

This video is very well done. Thank you for this content sir!

that was cool to watch, dude!

Amazing dude ❤️ deserve subscribe thanks for explaining

this is great!

Holy shit, this is probably the best explanation of anything ever, let alone CNN.

best cnn explanation

Excellent explanation in a very very simple way. Awesome.

Amazing explanation of CNN! Could you please do one for the "Attention is all you need" Transformer?

Exactly what I was looking for, I have forgotten details few times since info wasn't really connected well in my head. This makes it easy to understand and remember

This is one of best introductions I found on convolutional neural networks! Thank you so much!