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28 results

CodeEmporium
How to enhance performance of a Convolution Network? Feature Pyramid Networks - Explained!

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 ...

27:31
How to enhance performance of a Convolution Network? Feature Pyramid Networks - Explained!

635 views

1 month ago

CodeEmporium
Pointwise Convolutions - EXPLAINED (with code)

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

22:01
Pointwise Convolutions - EXPLAINED (with code)

875 views

3 months ago

CodeEmporium
Depthwise Separable Convolutions - Explained!

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

15:16
Depthwise Separable Convolutions - Explained!

836 views

1 month ago

CodeEmporium
Visualizing convolution networks

Let's visualize what a convolution neural network actually learns, their feature maps and more! ABOUT ME ⭕ Subscribe: ...

37:38
Visualizing convolution networks

1,254 views

5 months ago

Derek Harter
L09.3.5: A Mini Xception-like Model Example

This video is really a conclusion to the previous one where Iintroduced a few modern best practices and concepts for creating ...

12:00
L09.3.5: A Mini Xception-like Model Example

26 views

7 months ago

CodeEmporium
Convolution Network back propagation by hand | the math you should know!

In this video, we walk through the back propagation learning procedure in a convolution neural network, step by step. ABOUT ME ...

53:46
Convolution Network back propagation by hand | the math you should know!

1,197 views

6 months ago

CodeEmporium
Deconvolution - what do networks learn? (visualization + code)

In this video, we take a look at what the deep layers of a convolution neural network actually learn with some neat visuals.

22:44
Deconvolution - what do networks learn? (visualization + code)

1,010 views

4 months ago

CodeEmporium
Why convolution networks work so well (on images)

In this video, we talk through why convolution networks are so effective with image processing. ABOUT ME ⭕ Subscribe: ...

14:31
Why convolution networks work so well (on images)

1,361 views

5 months ago

CodeEmporium
R-CNN - Explained!

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

18:18
R-CNN - Explained!

1,839 views

4 months ago

The Debug Zone
How to Implement Zero Padding in Keras Convolutional Layers: A Step-by-Step Guide

In this video, we will explore the concept of zero padding in Keras convolutional layers, a crucial technique for enhancing the ...

2:17
How to Implement Zero Padding in Keras Convolutional Layers: A Step-by-Step Guide

27 views

5 months ago

CodeEmporium
Inception Net - Explained! (with code)

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?

15:46
Inception Net - Explained! (with code)

917 views

3 months ago

TU Delft Learning for Life
AIfE2x_2025_Module_3_1_Introduction_to_Computer_Vision_Image_and_Convolution-video

This educational video is part of the course: AI in Architectural Design: Introduction available for free via ...

9:44
AIfE2x_2025_Module_3_1_Introduction_to_Computer_Vision_Image_and_Convolution-video

14 views

4 months ago

Global Initiative of Academic Networks - GIAN
L 06 2D convolution, DFT, Optimal filtering

Medical Informatics, Radiomics, and Image Analysis for Computer-Aided Diagnosis Course Code: 2412136 Offered by: ...

1:31:17
L 06 2D convolution, DFT, Optimal filtering

7 views

6 months ago

The Debug Zone
How to Prepare Input Data for Conv1D in Keras: A Step-by-Step Guide

In this video, we will explore the essential steps for preparing input data for Conv1D layers in Keras, a powerful deep learning ...

2:42
How to Prepare Input Data for Conv1D in Keras: A Step-by-Step Guide

3 views

6 months ago

Derek Harter
L11.3.3: Two Approaches for Representing Groups of Words: Sequence models and word embeddings

In this video I continue with our talk about the two basic approaches that you can use to represent word order for processing text ...

35:01
L11.3.3: Two Approaches for Representing Groups of Words: Sequence models and word embeddings

29 views

7 months ago

CodeEmporium
Mask R-CNN - Explained!

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: ...

28:46
Mask R-CNN - Explained!

762 views

1 month ago

CodeEmporium
Boltzmann Machine - Explained!

Let's talk about Boltzmann Machines RESOURCES [1 ] Main paper: ...

23:53
Boltzmann Machine - Explained!

6,798 views

11 months ago

Data Science Learning Community Videos
Generative AI Handbook: Chapters 11, 12 (genai01 11 12)

David leads a discussion of Chapter 11 ("Encoders and Decoders") and Chapter 12 ("Decoder-Only Transformers") on ...

52:10
Generative AI Handbook: Chapters 11, 12 (genai01 11 12)

103 views

10 months ago

Naoki Shibata
Inside Shibatch Sample Rate Converter, balancing mathematical perfection with real-world performance

SSRC achieves 200dB stop-band attenuation and AVX-512 accelerated performance, utilizing SleefDFT to deliver speed without ...

33:07
Inside Shibatch Sample Rate Converter, balancing mathematical perfection with real-world performance

43 views

1 month ago

ojamboshop
Max Speed Wan 2.1: AMD Mi60 and ROCm Optimization Guide

Learn how to push the limits of AI video generation using the Wan 2.1 1.3B model on Fedora 43. This screencast demonstrates ...

31:47
Max Speed Wan 2.1: AMD Mi60 and ROCm Optimization Guide

67 views

Streamed 5 days ago