decentralized-ai
Tutorials & Insights
Thoughtful guides to help you get the most out of AIOZ AI R&D. Created and curated by the AIOZ AI team.
Decentralized AI
Take AIOZ AI Network from zero to production.
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2. Introduction to Federated Learning.
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3. Network Sytem and data silos for Federated Learning.
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4. Topologies in Decentralized Federated Learning.
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5. Smart Routing.
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6. AI-driven routing (part 1) Centralized Routing.
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7. AI-driven routing (part 2) Decentralize Routing.
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8. AI-driven routing (part 3) Hybrid Routing.
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9. AI-driven routing (part 4) Smart Routing Effectiveness.
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10. AI-driven routing (part 5) Challenges and Open Issues.
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11. Smart Caching.
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12. Smart Transcoding (part 1) Introduction.
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13. Smart Transcoding (part 2) Video Transcoding and Delivery with Blockchain.
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14. Smart Transcoding (part 3) Three-stage STACKELBERG game.
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15. Smart Transcoding (part 4) Effectiveness Evaluation.
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16. Video Compression (Part 1)
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17. Video Compression (Part 2)
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18. Decentralized Federated Learning and Research Directions (Part 1).
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19. Decentralized Federated Learning and Research Directions (Part 2).
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20. Foundation Model and Federated Learning (part 1).
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21. Foundation Model and Federated Learning (Part 2).
Computer Vision
If We Want Machines to Think, We Need to Teach Them to See.
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1. Visual Question Reasoning
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2. A quick guide on principal component analysis
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3. Exemplar Geometry-Aware Neural Style Transfer
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4. Pose-Controllable Talking Face Generation (part 1)
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5. Pose-Controllable Talking Face Generation (part 2)
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6. Video recognition and categorization (Part 1 - Introduction)
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7. Video recognition and categorization (Part 2 - Datasets and Evaluate Metrics)
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8. Video recognition and categorization (Part 3 - Applying RNN)
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9. Video recognition and categorization (Part 4 - Applying Deep Learning)
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10. Video recognition and categorization (Part 5 - Applying Deep Learning (cont.))
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11. Video recognition and categorization (Part 6 - A basic tutorial)
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12. Exemplar Geometry-Aware Neural Style Transfer (Part2)
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13. Representation Learning
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14. Abnormal Human Activity Recognition (Part 1 - Introduction)
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15. Relationship between energy map and seam carving
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16. Abnormal Human Activity Recognition (Part 2 - Two-Dimensional AbHAR)
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17. Vision Transformer
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18. Abnormal Human Activity Recognition (Part 3 - Two-Dimensional AbHAR (cont.))
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19. Abnormal Human Activity Recognition (Part 4 - Three-Dimensional AbHAR)
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20. Reproduce cartoon image
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21. Abnormal Human Activity Recognition (Part 5 - Three-Dimensional AbHAR) (cont.)
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22. A simple system for traffic anomaly detection
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23. Model Object Detection and Classification
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24. Abnormal Human Activity Recognition (Part 6 - Three-Dimensional AbHAR) (cont.)
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25. Abnormal Human Activity Recognition (Part 7 - Deep features based action description)
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26. Techniques for evaluating deep learning models.
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27. Transfer Learning
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28. Augmentation
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29. Abnormal Human Activity Recognition (Part 8 - Deep features based action description)
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30. Supervised representation learning
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31. Introduction to Diffusion Models
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32. A kind introduction to Multi Task Learning
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33. Multi Task Learning and its effectiveness
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34. Multi Task Learning and its correlation with Neural Network
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35. Summarization of common loss functions
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36. Abnormal Human Activity Recognition (Part 9 - Discussion)
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37. Introduction to Diffusion Models (Part 2)
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38. Autoencoder
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39. Variational Autoencoders (VAE)
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40. The overview of GAN (Generative Adversarial Networks)
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41. The overview of GAN - Part 2
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43. Object Affordance Detection
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44. Object Affordance Detection (Part 2)
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45. Abnormal Human Activity Recognition (Part 10 - Datasets)
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46. Abnormal Human Activity Recognition (Part 11 - Overall Summary)
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47. Latent Diffusion Models (Stable Diffusion)
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48. Bidirectional Encoder Representations from Transformers
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49. Diffusion Model: A breakthrough compared to Auto-encoder, GAN and Flow-based models
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Introduction to Diffusion Models (Part 3)
Robotics
A layman, with a fleeting understanding of technology, would link it to robots.
Computer Graphics
If it looks like computer graphics, it is not good computer graphics.
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1. Human Pose & Motion Capture exploration (part 1) Defenition and 2D Human Pose Estimation.
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2. Human Pose & Motion Capture exploration (part 2).
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3. Human Pose & Motion Capture exploration (part 3).
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4. Human Pose & Motion Capture exploration (part 4).
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5. Human Pose & Motion Capture exploration (part 5).
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6. Human Pose & Motion Capture exploration (part 6)
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7. Human Pose & Motion Capture exploration (part 7)
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8. Human Pose & Motion Capture exploration (part 8)
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9. Shape reconstruction from a single RGB video
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10. Applying Neural radiance field in 3D reconstruction from monocular video (Part 1)
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11. Applying Neural radiance field in 3D reconstruction from monocular video (Part 2)
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12. Character Animation and Skinning (part 1)
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13. Character Animation and Skinning (part 2)
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14. Introduction to Human Body Model
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15. Music-Driven Dance Generation
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16. Multi-person motion prediction
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17. Music-Driven Group Dance Generation
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18. Populating 3D Scenes by Learning Human Scene Interaction (Part 1).
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19. Populating 3D Scenes by Learning Human Scene Interaction (Part 2).
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20. Populating 3D Scenes by Learning Human Scene Interaction (Part 3).
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21. Minkowski Engine - part 1
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22. Minkowski Engine - part 2
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23. DanceNet for Music-driven Dance Generation
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24. Music to Multi-People Dance Synthesis with Style Collaboration.
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25. Method comparison between three latest Dance Generation approaches.
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26. Music2Dance and GroupDancer comparison.
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27. 3D Downstream Tasks for Foundation Model (Part 1)
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27. 3D Downstream Tasks for Foundation Model (Part 2).
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29. 3D Downstream Tasks for Foundation Model (Part 1).
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30. 3D Downstream Tasks for Foundation Model (Part2).
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31. 3D Downstream Tasks for Foundation Model (Part 1).
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32. 3D Downstream Tasks for Foundation Model (Part 2).
Medical Image Processing
The flashiest use of medical AI is to perform tasks that even the best human providers cannot yet do.
Machine Learning Operations (MLOps)
MLOps is the natural progression of DevOps in the context of AI.
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1. Model Serving and NVIDIA Triton Inference Server.
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2. Convert weight TF lite from Pytorch for Android app
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3. Pytorch Mobile
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4. Model Compression Part 1.
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5. Model Compression Part 2.
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6. Model Compression Part 3.
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7. Model Compression Part 4.
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8. Model Compression Part 5.
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9. Model Compression Part 6.
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10. Model Compression Part 7.
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11. Model Compression Part 8 (Lightweight variations of Squeeze U-Net).
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12. Benchmarking of GPEN, an image restoration method.
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13. Quantization in Deep learning
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14. Quantization on tensorflow
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