ml5.js: What is a Convolutional Neural Network Part 1 - Filters

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References:

Videos:

Timestamps:
0:00 Introduction
1:28 Review neural networks
3:34 Flattenng the image causes a loss of information
4:18 Convolutional layer
4:37 Filter
5:08 Gradient based learning
6:21 Filter is a matrix of numbers
6:59 Photoshop
7:14 Known filters draw out different features
8:59 Neural network learn filters that identify features
10:25 Blurring an image
11:12 Coding a convolutional neural network in p5js
11:56 Create a filtered image
13:10 Ignore the edge pixels
13:42 Convolution function
15:28 Finding the one-dimensional look-up into matrix
16:16 Four numbers are stored for every pixel
16:36 Sum r, g, b values
20:46 Multiply by filter
22:37 Get filtered pixels
23:13 Call loadPixels
23:34 Call updataPixels
24:40 Change the indices to find vertical edges
26:11 Add a random filter

Editing by Mathieu Blanchette
Animations by Jason Heglund
Music from Epidemic Sound

#convolutionalneuralnetwork #machinelearningml #ml5js #p5js

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Комментарии

This is what should be explained before teaching anyone about Convolutional neural networks.

Those photoshop custom filters & then the entire code thereon gave perfect visual intuition about convolutional layers.

Thanks it was such an enlightening video.

HankGussman

I bought a cnn course in udacity and let me tell you that you explain it way better. You really helped me to understand the whole picture of CNN.

illosuth

This is a really clear explanation of a convolutional nn. I've heard so many other people describe them, but your explanation is the clearest yet.

dr.mikeybee

Thank you sir so much, its individuals like you who actually make all the difference in the world

rohitmehta

OMG, in just two minutes you clearly explained the concept of convolution which I was struggling to understand after reading a few papers 🙏🏾!

umanagaswamy

4:38 - "Convolution Neural Networks can be applied to lots of scenarios besides images" - Dan, you sir are not only correct, you are fabulous for planting this seed early in the video. CNNs sure can be used for text, speech, music, and so much more than just images!

AntonPanchishin

i just got what i wanted. i was suffering from google searches about CNN and finally Daniel, you started this series. Thanks, keep going man.

rigelstar

Still the best explanation of Convolutional Neural Network anywhere on the internet.

CarloL

its been 2 years i'm following the channel, There is always something new to learn :)

basiccoder

One of the best videos for explaining filters Thank you!

msrahman

Thank you very much. I had to take a break in my Tensorflow class to understand convulutional filters better. This was a very good explanation. Thanks once again.

vallurirajesh

Hi Dan. I'd just like to thank you, you make everything much easier for me to understand. While I mainly focus on Python, your videos are still a massive help and source of entertainment. Thank you!

adammcgrogan

I really needed help with visualizing filters and this video helped me a lot! Thanks a bunch ^^

iwanturamichi

Your videos are really fun to watch, and has real depth in the subject at the same time. Quite unique on YouTube.

subinsebastien

I have been using photoshop for 20 years and had never really understood how the custom filter worked until now

DodaGarcia

I have bad english, even i dont understand what he was said, but i just love the way how this amazing man explain every single his video, i love it, this make me wanna hear more and more, im just student, try to learn front end, i love programming, hope i can being like him someday,

muhammadwahyuramadhan

Ohh the curious part of me feels so great after I watch any coding train videos even if it's not related to my work or any project that I am working on, It feels great appease my curiosity.
I wish there could would be a space train channel where Dan talks about space that would be ultimate. What a guy :)

vrdash

Thank you very much for making this. I was having trouble finding videos covering convolutional layers which attempted to cover the full concept of a filter.

tesseraction

Love this! Thank you for all that you do! You're an incredible teacher!

dsasilentsound

One of the best explanations on kernels (y)

anuragkurle

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