Difference between revisions of "NVIDIA"
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* [http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=&searchItems=&sessionTopic=&sessionEvent=2&sessionYear=2018&sessionFormat=&submit=&select= GTC Sessions] | * [http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=&searchItems=&sessionTopic=&sessionEvent=2&sessionYear=2018&sessionFormat=&submit=&select= GTC Sessions] | ||
* [[NVIDIA Jetson Nano]] | * [[NVIDIA Jetson Nano]] | ||
| + | * [http://developer.nvidia.com/transfer-learning-toolkit Transfer Learning Toolkit] a python-based toolkit that enables developers to take advantage of NVIDIA’s pre-trained models and offers capabilities for developers to adapt popular network architectures and backbones to their own data, train, fine tune, prune and export for deployment. The simple interface and abstraction improves the efficiency of the deep learning training workflow. | ||
* [http://www.nvidia.com/en-us/research/ai-playground/ NVIDIA Playground] | * [http://www.nvidia.com/en-us/research/ai-playground/ NVIDIA Playground] | ||
* [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU]] | * [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU]] | ||
Revision as of 17:20, 4 October 2019
Youtube search... ...Google search
- Platforms: Machine Learning as a Service (MLaaS)
- NVIDIA Developer
- NGC - NVIDIA GPU Cloud
- RAPIDS
- NVIDIA Deep Learning Institute
- GTC Sessions
- NVIDIA Jetson Nano
- Transfer Learning Toolkit a python-based toolkit that enables developers to take advantage of NVIDIA’s pre-trained models and offers capabilities for developers to adapt popular network architectures and backbones to their own data, train, fine tune, prune and export for deployment. The simple interface and abstraction improves the efficiency of the deep learning training workflow.
- NVIDIA Playground
- Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU
- NVIDIA GTC Conferences
- NVIDIA YouTube Videos
