Udemy - Introduction To Diffusion Models 2023

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[ DevCourseWeb.com ] Udemy - Introduction To Diffusion Models 2023
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1 - Introduction.mp4 (129.9 MB)
    2 - Initial paper on Diffusion Models
    • 10 - Reverse process sampling.mp4 (78.4 MB)
    • 11 - Reverse process visualization.mp4 (40.6 MB)
    • 12 - Training equations part 1.mp4 (72.7 MB)
    • 13 - Training equations part 2.mp4 (81.2 MB)
    • 14 - Training equations implementation part 1.mp4 (75.2 MB)
    • 15 - Training equations implementation part 2.mp4 (99.6 MB)
    • 16 - Implementation of the training loop.mp4 (58.8 MB)
    • 17 - Training on GPU.mp4 (86.1 MB)
    • 18 - Correct typo.mp4 (10.4 MB)
    • 19 - Reproduction of a Figure from the paper Analysis of the results.mp4 (28.8 MB)
    • 2 - Forward Diffusion process.mp4 (101.3 MB)
    • 3 - Forward Diffusion process implementation.mp4 (116.6 MB)
    • 4 - Diffusion process tricks.mp4 (30.0 MB)
    • 5 - Diffusion process incorporation of the tricks in the implementation.mp4 (77.9 MB)
    • 6 - Diffusion process visualization.mp4 (69.6 MB)
    • 7 - Reverse process.mp4 (58.6 MB)
    • 8 - Reverse process implementation.mp4 (30.6 MB)
    • 9 - Architecture of the model.mp4 (125.6 MB)
    3 - Denoising Diffusion Probabilistic Models
    • 20 - Review of the paper.mp4 (275.8 MB)
    • 21 - Time embedding.mp4 (47.3 MB)
    • 22 - Pseudocode.mp4 (51.0 MB)
    • 23 - UNet Implementation time embedding.mp4 (109.9 MB)
    • 24 - UNet Implementation downsampling.mp4 (100.9 MB)
    • 25 - UNet Implementation upsampling.mp4 (46.9 MB)
    • 26 - UNet Implementation ResNet part1.mp4 (108.1 MB)
    • 27 - UNet Implementation ResNet part2.mp4 (160.1 MB)
    • 28 - UNet Implementation ResNet part3.mp4 (27.5 MB)
    • 29 - UNet Implementation Attention Mechanism part1.mp4 (62.2 MB)
    • 30 - UNet Implementation Attention Mechanism part2.mp4 (9.9 MB)
    • 31 - Finishing the UNet Implementation part1.mp4 (156.0 MB)
    • 32 - Finishing the UNet Implementation part2.mp4 (50.1 MB)
    • 33 - Finishing the UNet Implementation part3.mp4 (24.1 MB)
    • 34 - Finishing the UNet Implementation part4.mp4 (126.7 MB)
    • 35 - Finishing the UNet Implementation part5.mp4 (44.2 MB)
    • 36 - Denoising Diffusion Probabilistic Models implementation.mp4 (42.8 MB)
    • 37 - Denoising Diffusion Probabilistic Models training.mp4 (55.2 MB)
    • 38 - Denoising Diffusion Probabilistic Models sampling.mp4 (93.8 MB)
    • 39 - Denoising Diffusion Probabilistic Models utils.mp4 (78.6 MB)
    • 40 - Denoising Diffusion Probabilistic Models training loop.mp4 (38.8 MB)
    • 41 - Denoising Diffusion Probabilistic Models visualization.mp4 (54.3 MB)
    • 42 - Denoising Diffusion Probabilistic Models training on GPU.mp4 (57.2 MB)
    • 43 - Analysis of the results.mp4 (11.6 MB)
    4 - Inpainting
    • 44 - Inpainting with Diffusion Models explanation.mp4 (97.6 MB)
    • 45 - Inpainting with Diffusion Models implementation.mp4 (187.1 MB)
    5 - Animating Diffusion Models
    • 46 - Animations part1.mp4 (96.7 MB)
    • 47 - Animations part2.mp4 (91.7 MB)
    • 48 - Animations part3.mp4 (47.5 MB)
    • Bonus Resources.txt (0.4 KB)

Description

Introduction To Diffusion Models 2023



https://DevCourseWeb.com

Published 5/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.64 GB | Duration: 8h 3m

Diffusion Models from scratch using PyToch | In depth breaking down of Stable Diffusion and DALL-E

What you'll learn
How Diffusion Models work
Implementation of Diffusion Models from scratch using PyTorch
In depth understanding of inpainting with Diffusion Models
Deep analysis of Stable Diffusion: opening the black box
Making great animations with Diffusion Models
Review of impactful research papers

Requirements
Basic programming knowledge
Basic Machine Learning knowledge



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Udemy - Introduction To Diffusion Models 2023

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3.6 GB
seeders:2
leechers:15
Udemy - Introduction To Diffusion Models 2023

Torrent hash: 6E396415070AD6DBC8C01504A689EDA83079F4C5