2023年秋季 • 麻省理工学院
麻省理工学院“TinyML语高效深度学习计算”课程，涵盖了、模型压缩、剪枝、量化、神经架构搜索、分布式训练、数据/模型并行、梯度压缩以及设备端微调等主题】《6.5940 TinyML and Efficient Deep Learning Computing》
大型生成模型（例如大型语言模型、扩散模型）表现出非凡的性能，但它们需要大量的计算资源。为了使它们更容易获得，提高它们的效率至关重要。
课程地址：https://efficientml.ai/schedule/
本课程将介绍高效的人工智能计算技术，这些技术可以在资源有限的设备上实现强大的深度学习应用程序。主题包括模型压缩、修剪、量化、神经架构搜索、分布式训练、数据/模型并行性、梯度压缩和设备上微调。它还为大型语言模型、扩散模型、视频识别和点云引入了特定于应用程序的加速技术。本课程还将涵盖有关量子机器学习的主题。学生将获得在笔记本电脑上部署大型语言模型（例如LLaMA 2）的实践经验。
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