MIT 6.S191: Convolutional Neural Networks

MIT Introduction to Deep Learning 6.S191: Lecture 3
Convolutional Neural Networks for Computer Vision
Lecturer: Alexander Amini
* New 2024 Edition *
For all lectures, slides, and lab materials: introtodeeplearning.com​
Lecture Outline
0:00​ - Introduction
2:45​ - Amazing applications of vision
4:56 - What computers "see"
13:09- Learning visual features
18:53​ - Feature extraction and convolution
22:12 - The convolution operation
28:38​ - Convolution neural networks
37:10​ - Non-linearity and pooling
41:23 - End-to-end code example
43:21​ - Applications
46:14 - Object detection
57:10 - End-to-end self driving cars
1:06:15​ - Summary
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Thank you for sharing quality content like this for free for several years

[@DreamBuilders-rq6km]

Thanks for sharing this knowledge. Be blessed

[@karterel4562]

thank for sharing that course , that's so usefull !

[@PerceptronsAI]

I wanted to extend my sincere thanks for the wonderful lecture you delivered on Deep Learning.

[@woodworkingaspirations1720]

Waiting patiently

[@AshokKumar-mg1wx]

That's the spirit

[@ghaithal-refai4550]

Thank you very much, it is a great lecture. I hope that you develop the lectures over the years as it seems to be the same contents. topics like pretrained models and knowledge transfer, YOLO might be good to be added to CNN

[@htoorutube]

Software Lab 1 still not made available, when will that happen?

[@RajeevKumar-dq4ct]

It is published now

[@genkideska4486]

Waiting ..

[@vijaykumars1771]

Thank you, i have one doubt here, at 15:30 you said 10 k neurons in hidden layer for processing 10k parameters, so resultant would be 10k^2 parameters. My doubt is why we need 10 k neurons at any layer. we can decide the number of layers right?

[@MySouvik]

While sliding window is good, YoLo outperforms Faster RCNN and is generally considered state of the art for object detection

[@4threich166]

Where is the software lab?

[@meshkatuddinahammed]

I have a confusion about the Lab 2 Part 2 ( facial Detection with CNN). It has been claimed that in the CelebA dataset most faces are of light skinned females. But the model ultimately gives lower accuracy for this category of faces compared to other three categories. Why is that?

[@samiragh63]

Cant wait...

[@jorgeguiragossian8488]

Have any of the labs been published yet?

[@RajeevKumar-dq4ct]

yes

[@shahriarahmadfahim6457]

But the lab between Lecture 2 and 3 is still not published in the website?

[@benjaminy.]

I think it is not their practice to publish their lab work

[@RajeevKumar-dq4ct]

It has been published now

[@tmcgraw]

right?

[@abdelazizeabdullahelsouday8118]

Thank you for sharing, please i need a help and i send an email to you but no response, could you please help me?
thanks in advance.

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