update

docs/机器学习系统/CSE234.en.md

@@ -20,6 +20,7 @@ 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):
 
 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  
@@ -27,6 +28,7 @@ Part 1. Foundations: modern deep learning and computational representations
 
 
 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  
@@ -35,6 +37,7 @@ Part 2. Systems and performance optimization: from GPU kernels to compilation an
 
 
 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

@@ -22,12 +22,14 @@
 课程可以更准确地分为三个部分（外加若干 guest lecture）：
 
 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）
 
 
 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）
@@ -36,6 +38,7 @@ Part 2. 系统与性能优化：从 GPU Kernel 到编译与内存
 
 
 Part 3. LLM系统：训练与推理
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 - 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