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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 at Feature Pyramid Networks (FPN). What is it? How does it work? Why they are so useful in computer ...

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

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 ...

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 the Mask R-CNN network. What is it? How is it trained? Code for inference! ABOUT ME ⭕ Subscribe: ...

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

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

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: ...

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

In this video, we take a look the inception network architecture. What is it? What does it look so funky? How do we code it out?

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 ...