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llyg0102.github.io's Introduction

开始

2023.5.21:本文档用于记录学习过程中的一些笔记,主要包括分子动力学机器学习两个方面。分子动力学部分主要记录MD的基本知识,常用的优化算法,以及机器学习上处理MD数据的方法;机器学习部分主要记录机器学习的基本知识,以及各种成型的网络结构和特点。最后,记录一些最新的文献和他们的方法。

2024.3.21:重新开始记录,目前希望可以探索更多的大模型知识,大模型是趋势,即使硬件水平不达标,也要尽可能将模型做大才能得到好性能。同时了解最新的大模型知识,大模型搭建经验,尝试通过fine tunning建立一个自己的模型。

参考的教材

数学:

  1. 《线性代数及其应用》 David C. Lay
  2. 《现代数学基础(第二版)》 陆起超
  3. 《矩阵分析》Roger A. Horn

计算化学:

  1. 《结构化学》 孙宏伟

机器学习(包括深度学习):

  1. 《神经网络与深度学习》 邱锡鹏
  2. 《统计学习方法》 李航
  3. 《机器学习》 周志华

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