NVIDIA held the GTC Technology Conference in Beijing today. During the conference, NVIDIA Deep Learning Institute (hereinafter referred to as DLI) announced its official entry into China, and cooperated with Chinese companies such as Leadtek to launch online training and instructor-led offline seminars for artificial intelligence developers and researchers. Data scientists provide hands-on lab training courses.

NVIDIA deep learning to enter China _ help companies train AI talents

Greg Estes, vice president of the NVIDIA Developer Program, said: "Artificial intelligence is sweeping the technology environment. In more and more areas, deep learning expertise will become a core skill." According to IDC, by 2020, 80% The application will be related to AI and the demand is large.

Greg Estes announced that DLI will join the Chinese course. In addition to teaching developers how to develop on the AI ​​reasoning platform TensorRT, there will be basic courses such as how to do image classification and object recognition. In addition, there will be various AI algorithm development for financial industry data scientists, how to use the image recognition to quickly and accurately find cancer cells. Greg Estes said that in the future, DLI hopes to train 20,000 developers in China, accounting for one-fifth of the global training.

DLI has only been established for a year, and has accumulated more than 20,000 developers worldwide. DLI has been global with AWS, Coursera, Facebook, Google, Hewlett-Packard Enterprise, IBM, Microsoft and Udacity. Technology leaders and online training partners have collaborated to provide developers with training on the latest artificial intelligence technologies using all major deep learning frameworks.

It is reported that during GTC CHINA, DLI will provide participants with a three-day intensive study training course, and for the first time open 10 new in-depth courses for Chinese students, as well as more than ten offline training courses. The content includes image classification, deployment of neural networks, image generation, genomics, etc. It will provide developers, data scientists and researchers with the practical experience needed for deep learning applications in multiple application areas.

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