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DeepStream application architecture
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Classroom Contents
Best Practices for Real-time Intelligent Video Analytics
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- 1 Intro
- 2 Why intelligent video analytics?
- 3 Challenges with intelligent video analytics
- 4 Tips & tricks for efficient AI video analytics
- 5 Transfer learning
- 6 Data augmentation
- 7 Automatic mixed precision AMP
- 8 Quantization
- 9 Network pruning
- 10 Network graph optimizations
- 11 Kernel auto-tuning
- 12 Dynamic tensor memory upon inference
- 13 Multistream concurent execution
- 14 Free NVIDIA products designed to make your AI App efficient
- 15 NVIDIA's end-to-end AI workflow
- 16 TAO toolkit
- 17 high performance pre-trained vision AI models
- 18 Enabling beyond pre-trained AI models
- 19 Achieving state of the art accuracy for public datasets
- 20 NVIDIA TensorRT
- 21 TensorRT workflow
- 22 Triton inference server
- 23 DeepStream SDK
- 24 DeepStream application architecture
- 25 Pipelin efficiency with zero memory copies
- 26 NVIDIA graph composer
- 27 DeepStream video demo
- 28 Summary
- 29 Developer resources
- 30 Outro
