This video explains how Convolutional Neural Networks (CNNs) work for image recognition and computer vision, starting from a simple neural network and showing why images require a different architecture. It covers the key ideas behind convolution operations, kernels, feature maps, multi-channel inputs (RGB), pooling layers, and the full CNN pipeline used in deep learning. The video also explains the important inductive biases of CNNs, including local connectivity, translation equivariance, parameter sharing, translation invariance, and hierarchical feature learning, which make CNNs powerful for processing visual data. *Related Videos* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The Hessian Matrix: https://youtu.be/9tp1kULwU2w The Jacobian Matrix: https://youtu.be/6FesMicc844 Bayesian Optimization: https://youtu.be/Kq6_kzlwSUQ Hyperparameters Tuning: Grid Search vs Random Search: https://youtu.be/G-fXV-o9QV8 The Kernel Trick: https://youtu.be/N_RQj4OL1mg Cross-Entropy - Explained: https://youtu.be/Fv98vtitmiA Dropout - Explained: https://youtu.be/FDF_Q3_98GQ Overfitting vs Underfitting: https://youtu.be/B9rhzg6_LLw Why Models Overfit and Underfit - The Bias Variance Trade-off: https://youtu.be/5mbX6ITznHk Least Squares vs Maximum Likelihood: https://youtu.be/WCP98USBZ0w *Contents* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 00:00 - Intro 01:28 - The convolution operation 02:38 - Multiple kernels 03:25 - Multi-channel input 04:34 - The full CNN pipeline 05:48 - Max pooling 07:18 - Inductive biases *Follow Me* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 X: @datamlistic https://x.com/datamlistic 📸 Instagram: @datamlistic https://www.instagram.com/datamlistic 📱 TikTok: @datamlistic https://www.tiktok.com/@datamlistic 👔 Linkedin: https://www.linkedin.com/company/datamlistic *Channel Support* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon: https://www.patreon.com/datamlistic ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #deeplearning #neuralnetworks #computervision #machinelearning #cnn
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I asked ai about the CNN it gave me same explanation and same example LOL either you created explanation vai AI (i dont care how you did it) or The Ai extracted your video and gave same explanation. Very interesting.
📚 CNNs are the workhorse of computer vision - convolution + pooling + nonlinearity, stacked. Chollet's "Deep Learning with Python" (by the Keras author) is the cleanest practical take: /4d1t1aj (Affiliate link - supports the channel at no extra cost to you 🙏)
Bro this is gold. This feels like THE BEST summary or quick recap but is also pretty self sufficient intro. Bro this ought to have lot more views.
Great video! Can you make a video about predictive coding vs backpropagation next please?
This is an excellent learning resource, thank you for taking the time to make this
the very best CNN video
amazing explaination!
omg this video made me realize i misunderstood how conv layers affect dimensions. This video probably just saved my life on my exam tmrw
Very nicely explained. Thanks.
This video about neural networks is actually golden? Thanks for making it so intuitive. If you can, please consider making a video on Xai, or AI with reasoning, that could be cool :)
at 2:03 isnt add meant to equal positive 400 not negative 400 since -200(-1) + (-200)(-1) = +400