BROWSE GTC 2019 SESSIONS

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顶级 AI 盛会

NVIDIA GTC (GPU 技术大会) 是一系列全球盛会， 为您带来当今计算领域最热门话题的相关培训和见解，并为您创造直接接触顶级专家的机会。
建立沟通

建立沟通

与 NVIDIA 及众多业界领先的技术专家近距离沟通和互动。

热点话题

热点话题

了解当下最新的主题和行业相关内容。

顶级会议

顶级会议

百余场顶尖专家带来的演讲任您挑选参加。

价值培训

价值培训

通过在云端 GPU 平台上的动手实践培训，获得可用于工作中的实用技能。

AI 相关主题

AI 框架与研究
AI 框架及应用
开发工具
AI 开发及工具
科学计算中的 AI
科学计算中的 AI
数据中心和云计算
数据中心,
云计算和图形虚拟化
加速数据科学 加速数据科学
加速数据科学
智慧医疗和生命科学
智慧医疗和生命科学
智慧金融
智慧金融
边缘计算 / AI + 5G 赋能行业
边缘计算 /
AI + 5G 赋能行业
自动驾驶与交通
自动驾驶与交通
智能机器、机器人 和物联网
自主机器:机器人和
物联网

图形相关主题

游戏
光线追踪在游戏中的应用
工业制造与建筑工程
工业设计与工程
产品渲染和光线追踪
产品渲染和光线追踪
工业制造与建筑工程
AI 加速图形应用

2019 演讲者

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今年 GTC 的演讲者名单可谓是当今 AI 与深度学习的名人录…… 我一年参加近 75 场活动，而 GTC 是我不容错过的。

- Patrick Moorhead, 福布斯

从自动驾驶……机器人和无人机转向一般 AI，GTC 涵盖了本世纪以来我们重新定义世界的方方面面。

- Rob Enderle, TGDaily

无论是开发人员、数据科学家还是商务人士， 只要您关注 AI 和深度学习， GTC 都是最佳去处。

- Patrick Moorhead，福布斯

如果 GPU 是时光机，那么 GTC 就是窥探未来的窗口。

– Jensen Huang, NVIDIA

联系我们

如您对 GTC CHINA 有任何官网无法解答的疑问，如赞助商、展位、注册、市场推广、媒体报道等，请发邮件至 GTC_China_Contact@nvidia.com 咨询，我们将尽快予以回复。

DEEP LEARNING FOR FULL-MOTION VIDEO ANALYTICS

Prerequisites: Basic experience with CNNs and C++
Abstract: Smart cities collect huge amounts of video footage that require advanced deep learning techniques to transform data into actionable insights. The first step in more complex deep learning workflows is detecting specific types of objects. This involves identification, classification, segmentation, prediction, and recommendation. In this workshop, you’ll learn how to:

  • Train and evaluate deep learning models using the TensorFlow object detection application programming interface (API)
  • Explore the strategies and trade-offs involved in developing high quality neural network models to track moving objects in large-scale video datasets
  • Optimize inference times using TensorRT for real-time applications

Upon completion, you’ll be able to deploy object detection and tracking networks to work on real-time, large-scale video streams.

FUNDAMENTALS OF DEEP LEARNING FOR NATURAL LANGUAGE PROCESSING

Prerequisites: Basic experience with Neural Networks
Abstract: Learn the latest deep learning techniques to understand textual input using natural language processing (NLP). You’ll learn how to:

  • Convert text to machine-understandable representation and classical approaches
  • Implement distributed representations (embeddings) and understand their properties
  • Train machine translators from one language to another

Upon completion, you’ll be proficient in NLP using embeddings in similar applications.

FUNDAMENTALS OF DEEP LEARNING FOR MULTI-GPUS

Prerequisites: Experience with stochastic gradient descent mechanics
Abstract: The computational requirements of deep neural networks used to enable AI applications like self-driving cars are enormous. A single training cycle can take weeks on a single GPU, or even years for the larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible. This workshop will teach you how to use multiple GPUs to training neural networks. You'll learn:

  • Approaches to multi-GPU training
  • Algorithmic and engineering challenges to large-scale training
  • Key techniques used to overcome the challenges mentioned above

Upon completion, you'll be able to effectively parallelize training of deep neural networks using TensorFlow.

FUNDAMENTALS OF ACCELERATED COMPUTING WITH CUDA PYTHON

Prerequisites: Basic experience with Python and NumPy
Abstract: This workshop explores how to use Numba—the just-in-time, type-specializing Python function compiler—to accelerate Python programs to run on massively parallel NVIDIA GPUs. You’ll learn how to:

  • Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs)
  • Use Numba to create and launch custom CUDA kernels
  • Apply key GPU memory management techniques

Upon completion, you’ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.

Deep Learning for Robotics

Prerequisites: Basic experience with CNNs, circuits, and hardware.
Certification available: Explore how to create robotic solutions on an NVIDIA Jetson for embedded applications by applying computer vision models to perform detection and training a robot to actuate the correct output.

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