Diffusion Models (1/2) - Theory and importance with code implementations

In this part 1/2 video, you will learn Diffusion Models with their theory and importance in text-to-image AI research along with various code implementations located at the GitHub.

Part 1 (This Video): (https://youtu.be/W2udr9Z73sA)

1. Theory behind Diffusion
2. How it works?
3. Why it is important?
4. Various implementation available in GitHub

Part 2: (https://youtu.be/0uNri-aLk_o)

1. Probabilistic Diffusion Models Code Implementation
2. Prebuilt Models Note: - Try both in Google Colab Notebook

GitHub Resources: https://github.com/prodramp/DeepWorks/tree/main/DiffusionModels

▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬

• (00:00) Diffusion Model Intro
• (02:00) Part 1/2 Contents
• (04:05) Diffusion Model Theory
• (08:15) Importance
• (11:19) Various Code Implementations
• (13:18) Resources at GitHub

Connect

• Prodramp LLC (@prodramp)
• Website - https://prodramp.com
• LinkedIn - https://www.linkedin.com/company/prodramp
• GitHub- https://github.com/prodramp/
• AngelList - https://angel.co/company/prodramp
• Facebook - https://www.facebook.com/Prodramp

Content Creator: Avkash Chauhan (@avkashchauhan)

• https://www.linkedin.com/in/avkashchauhan
• https://twitter.com/avkashchauhan

Tags: #diffusion #ai #deeplearning #cnn #ml #lime #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #pytorch #datarobot #datahub #streamlit #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #kaggle #mapbox #lightgbm #xgboost #classification #dataengineering #pandas #keras #tensorflow #tensorboard #cnn #prodramp #avkashchauhan #LIME #mli #xai ... https://www.youtube.com/watch?v=W2udr9Z73sA