IAP09 CUDA@MIT 6.963 - Lecture 01: Logistics (Nicolas Pinto, MIT)

運用 NVIDIA JETSON TX1 開發者套件進行圖像分類與物體檢測課程目標與介紹學習構建一個從訓練數據庫到智慧終端深度學習流程。在本課程終您將實際操作開發技能，不僅訓練深層神經網絡，而且還將如何在生產環境中進行部署。在本實驗中，您可以將預先訓練的圖像分類和物體檢測的神經網絡，並將其部署在 Jetson TX1 或 TX2 開發者套件上，接著使用內建攝影機測試這些網絡，以分類和檢測幾個真實世界的對象。網絡將部署在各種編程環境中，我們甚至將介紹如何使用 NVIDIA 的 TensorRT 推理引擎庫在運行時優化分類和檢測性能。

See http://sites.google.com/site/cudaiap2009 and http://pinto.scripts.mit.edu/Classes/CUDAIAP2009

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IAP0

Supercomputing on your desktop:
Programming the next generation of cheap
and massively parallel hardware using CUDA

Lecture 01
Nicolas Pinto (MIT)

Kick - Off session

Still doing your
computations the old way?

Fresh New
Technology
Available NOW!

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IAP
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6.9

Course Goals
• Learn how to program massively parallel
processors and achieve
–high performance
–functionality and maintainability
–scalability across future generations
• Acquire technical knowledge required to
achieve the above goals
–principles and patterns of parallel programming
–processor architecture features and constraints
–programming API, tools and techniques
6.963
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apte
© David Kirk/NVIDIA and Wen-mei W. Hwu, 2007

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ECE 498AL1, University of Illinois, Urbana-Champaign

Class logistics
Teaching Staff (MIT)

GPU Computing with
CUDA David Luebke (NVIDIA)
CUDA Demos
Marc Adams (NVIDIA)

Class logistics
Teaching Staff (MIT)

GPU Computing with
CUDA David Luebke (NVIDIA)
CUDA Demos
Marc Adams (NVIDIA)

High-Throughput
Scientiﬁc Computing
Hanspeter Pﬁster (Harvard)

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Faculty: Prof. Steven G. Johnson

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TAs: Justin Riley and Nicolas Poilvert

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Instructor: Nicolas Pinto
Contact: pinto@mit.edu

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Lectures: M/W/F 10-12 (#32-155)
HandsOn: M/W/F 2-5 (#32-141)

Project Hours: T/R 2-5 (#3-370)

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/ CUDA Advanced
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(OEIT, DiCarlo Lab, Graphics CSAIL, EECS)

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3) Assignments
4) Enjoy!
Contact: pinto@mit.edu

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