add cse 234 w25
This commit is contained in:
parent
a98b1be31c
commit
77a98b09ff
|
|
@ -14,7 +14,7 @@ The curriculum is divided into three main sections:
|
|||
|
||||
1. **Fundamentals**: Covers topics such as deep learning, automatic differentiation, and an overview of machine learning systems.
|
||||
|
||||
2. **Machine Learning Systems and Optimization**: Includes subjects like dataflow graph systems, machine learning compilation, memory and graph optimizations, and distributed machine learning optimization.
|
||||
2. **Machine Learning Systems and Optimization**: Includes subjects like machine learning compilation, memory and graph optimizations, and distributed machine learning optimization.
|
||||
|
||||
3. **Large (Language) Models**: Explores cutting-edge topics such as training of large language models (LLMs), data preparation, inference and serving, attention mechanism optimization, and retrieval-augmented generation (RAG).
|
||||
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@
|
|||
|
||||
1. 基础知识:包括深度学习、自动微分、机器学习系统概述等。
|
||||
|
||||
2. 机器学习系统与优化:涵盖数据流图系统、机器学习编译、内存与图优化、分布式机器学习优化等主题。
|
||||
2. 机器学习系统与优化:机器学习编译、内存与图优化、分布式机器学习优化等主题。
|
||||
|
||||
3. 大(语言)模型:探讨LLM的训练、数据准备、推理与服务、注意力机制优化、检索增强生成(RAG)等前沿话题。
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue