Convolutional Neural Networks (CNNs) explained

43 thoughts on “Convolutional Neural Networks (CNNs) explained”

1. Check out the corresponding blog and other resources for this video at: http://deeplizard.com/learn/video/YRhxdVk_sIs

2. Great video!!! Very good tutor too, helped me understand straightforward the CNN's.

3. Finally I understand that !!! Thank you so much

4. loved it..

5. really good work, congrats!

6. Even if you've written down what you were saying, not stuttering or mincing words is pretty amazing!!!!

7. Halo,
   So in the example that mentioned in this video, a filter is used and convolved over the image "7" and recreated an output of "7" by doing dot product…..So my question is that whether this is similar to autoencoders?….because, in both a recreation of the input at the output is happening…right?

8. Halo,
   I would like to know regarding flattening in deep learning, and also………
   I would like to know how to create a new image dataset ?…….whether we should concern about image resolution and other criteria while downloading and saving…..etc….

9. Hey deeplizard,

   thanks for the great video (and series)! I perceive your explanations as very clear and audience-focused. I really enjoy your videos! 🙂

   I was wondering how exactly convolution works. You said that the convolution operation you perform is based on a dot product between the filter and a given patch of the data. I tried to simulate this process and it seems to me that to get the same results as in your excel simulation one should rather perform element-wise multiplication and then sum all the elements of the resulting matrix. How do you think about it?

   Thanks again for your effort and great content! <3

10. perfect video for understanding (CNNs)

11. Highly underrated. Great Video. Liked it before the video even ended!

12. The Excel example was beautiful! Quick question: does the size of the image that is passed decrease over each layer? I didn't count the rows and columns of the dot product result, but intuitively it seems that it will decrease?

13. Great video not only to understand CNNs, but convolution as a concept in particular. Seems like everyone agrees, that Excel example was on point!

14. Great video, thanks very much.

15. Nice video! Thank You! From SNU 🙂

16. Ahhhhhhh…so that's what convolutional means. Much obliged.

17. Thanks a lot.

18. Keep up the good work! Like the visual explanation

19. A great visual explanation, thank you ! It really helps understanding the concept

20. very clear

21. we don't dot product the filter with a region on input. it's an element wise multiplication , each cell times the corresponding cell and then we sum them all to get one output.

22. Thank you Ma'am your videos are great 🙂

23. How can you store the value of the dot product of two 3×3 matrices in a single value if the result is a 3×3 matrix..

24. I understood the concept very clearly…Thank u so much for this video…

25. Very clear and well done. Thanks for making this.

26. start : 0:14

27. What is the formula for the dot product? I do not know what it means to perform a dot product of two 3×3 matrices.

28. Great video, and very good channel as well!

29. Great job!

30. Great! Thanks a lot! I enjoyed the view on how to define the filters for edges! Saw the light! I will be following your next updates!

31. Nice short conceptual explanation!

32. You should have said that the dot product was a weighted sum

33. Always had a hard time trying to understand and visualize a CNN – this video really broke ground for in that regard

34. As always, I totally love and appreciate your videos. In this one I hoped to get pooling explained, though 😉

35. Thorough and to the point, love it

36. Is that a robat voice ?

37. This was one of the best explanations on Conv Nets outs there! Thanks!

38. I really like the way you explain. No matter if you're new to this topic or just refreshing from a different POV. Subscribed and thanks!

39. Isn't the sound at the end coming from Dishonnored? :O
    Great video tho, really helped to clarify things

40. Thank you so much! It help me to understand CNN

41. The best Video on cnn. Thank you very much

42. This extremely neat and helpful. Thank you.

43. Great explanation!

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