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In this video, we take a look at depthwise separable convolutions. What is it? How does it work? Why do it? Code included!

Welcome to Swayam Prabha Subject: Electrical Engineering Course Name: An Introduction to Coding Theory Name of ...

In this video, we take a look at CLIP (contrastive language image pretraining). What is it? Why do we have it? How does it look?

Welcome to Swayam Prabha Subject: Electrical Engineering Course Name: Information Theory, Coding and Cryptography ...

Welcome to Swayam Prabha Subject: Electrical Engineering Course Name: Information Theory, Coding and Cryptography ...

In this video, we take a look the ResNet network. What is it? Why is it better than some of the shallower networks that came before ...

Jupyter-notebook at https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/week5/ipynb/week5.ipynb.

In this video, we take a look at Feature Pyramid Networks (FPN). What is it? How does it work? Why they are so useful in computer ...

Lecture 15: Trellis Representations of Binary Linear Block Codes Topics covered: Trellis Representations of Binary Linear Block ...

In this video, we take a look the Fast RCNN network. What is it? Why is it better than the original RCNN? How do we train and ...

Welcome to Swayam Prabha Subject: Electrical Engineering Course Name: Information Theory, Coding and Cryptography ...

In this video, we take a look 1x1 convolutions (point wise convolutions) and demonstrate what they are, why they are useful, ...

In this video, we take a look at R-CNN (regions with convolution features). We see how training and inference occurs. ABOUT ME ...

In this video, we take a look the Mask R-CNN network. What is it? How is it trained? Code for inference! ABOUT ME ⭕ Subscribe: ...

Welcome to Swayam Prabha Subject: Electrical Engineering Course Name: Information Theory, Coding and Cryptography ...

In this video, we take a look the YOLO (V1) network. What is it? Why and how does it work? ABOUT ME ⭕ Subscribe: ...

So now that the Einste makes sense let's go back to this this code over here So we have uh essentially so B O X those are the ...