Site updated: 2025-08-26 17:50:25

Author: Big-TRex

2025/06/03/hello-world/index.html

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   <meta name="description" content="Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub. Quick">
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2025/06/10/A0-onboarding/index.html

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   <meta name="description" content="本作业旨在帮助你熟悉编程环境、提交流程以及基本的 PyTorch 编程。通过完成它，你将确保开发环境配置正确，理解如何提交未来的作业，并加强 PyTorch 编程技能。 Environment SetupOption 1: Local Setup Python: 3.10 或更高版本   Packages: 推荐通过以下命令安装所有必要的依赖项：  1pip install -r requirem">
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2025/06/14/A1-matmul/index.html

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   <meta name="description" content="在本次实验中，我们将实现深度学习模型中非常核心且基础的运算模块——矩阵乘（Matrix Multiplication）。矩阵乘法不仅是构建神经网络中全连接层、卷积操作和注意力机制等模块的基础，同时也是高性能计算优化的重要对象。 Task 1: MalMul with multi-head variant在 task 1 中，我们要实现两个矩阵相乘的逻辑，我们有以下两个矩阵：  A1：一个 3D 的">
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2025/06/17/A2-norm-emb/index.html

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   <meta name="description" content="在本次实验中，我们将实现 Transfomer 中的两个关键模块：均方根层归一化（RMSNorm）和词嵌入（Embedding）层。RMSNorm 是一种高效的归一化方法，通过仅利用输入的均方根值进行归一化，在保持模型性能的前提下，提升了计算效率与训练稳定性。Embedding 层则广泛应用于自然语言处理任务，其主要作用是将离散的符号（如单词或子词）映射为稠密的向量表示，从而为模型提供可学习的语义">
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 <meta property="og:title" content="A2 RMSNorm and Embedding">
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 <meta property="og:description" content="在本次实验中，我们将实现 Transfomer 中的两个关键模块：均方根层归一化（RMSNorm）和词嵌入（Embedding）层。RMSNorm 是一种高效的归一化方法，通过仅利用输入的均方根值进行归一化，在保持模型性能的前提下，提升了计算效率与训练稳定性。Embedding 层则广泛应用于自然语言处理任务，其主要作用是将离散的符号（如单词或子词）映射为稠密的向量表示，从而为模型提供可学习的语义">
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