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docs/机器学习系统/CSE234.en.md

@@ -19,19 +19,19 @@ This course focuses on the design of end-to-end large language model (LLM) syste
 
 The course can be more accurately divided into three parts (with several additional guest lectures):
 
-1. Foundations: modern deep learning and computational representations  
+Part 1. Foundations: modern deep learning and computational representations  
    - Modern deep learning and computation graphs (framework and system fundamentals)  
    - Automatic differentiation and an overview of ML system architectures  
    - Tensor formats, in-depth matrix multiplication, and hardware accelerators  
 
-2. Systems and performance optimization: from GPU kernels to compilation and memory  
+Part 2. Systems and performance optimization: from GPU kernels to compilation and memory  
    - GPUs and CUDA (including basic performance models)  
    - GPU matrix multiplication and operator-level compilation  
    - Triton programming, graph optimization, and compilation  
    - Memory management (including practical issues and techniques in training and inference)  
    - Quantization methods and system-level deployment  
 
-3. LLM systems: training and inference  
+Part 3. LLM systems: training and inference  
    - Parallelization strategies: model parallelism, collective communication, intra-/inter-op parallelism, and auto-parallelization  
    - LLM fundamentals: Transformers, Attention, and MoE  
    - LLM training optimizations (e.g., FlashAttention-style techniques)  

docs/机器学习系统/CSE234.md

@@ -21,19 +21,19 @@
 
 课程可以更准确地分为三个部分（外加若干 guest lecture）：
 
-1. 基础：现代深度学习与计算表示
+Part 1. 基础：现代深度学习与计算表示
    - Modern DL 与计算图（computational graph / framework 基础）
    - Autodiff 与 ML system 架构概览
    - Tensor format、MatMul 深入与硬件加速器（accelerators）
 
-2. 系统与性能优化：从 GPU Kernel 到编译与内存
+Part 2. 系统与性能优化：从 GPU Kernel 到编译与内存
    - GPUs & CUDA（含基本性能模型）
    - GPU MatMul 与算子编译（operator compilation）
    - Triton 编程、图优化与编译（graph optimization & compilation）
    - Memory（含训练/推理中的内存问题与技巧）
    - Quantization（量化方法与系统落地）
 
-3. LLM系统：训练与推理
+Part 3. LLM系统：训练与推理
    - 并行策略：模型并行、collective communication、intra-/inter-op、自动并行化
    - LLM 基础：Transformer、Attention、MoE
    - LLM 训练优化：FlashAttention 等