
modern_ai_for_beginners
modern AI for beginners
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This repository provides a comprehensive guide to modern AI for beginners, covering both theoretical foundations and practical implementation. It emphasizes the importance of understanding both the mathematical principles and the code implementation of AI models. The repository includes resources on PyTorch, deep learning fundamentals, mathematical foundations, transformer-based LLMs, diffusion models, software engineering, and full-stack development. It also features tutorials on natural language processing with transformers, reinforcement learning, and practical deep learning for coders.
README:
我一直想在我的所有的B站系列视频想传递的一个概念是,既要懂代码实现,也要懂数学计算原理,或者反过来,不仅要懂原理,也要会实现; 原理与实践二者互为表里,不可分割。再者来说,实现是相对简单的,原理则是困难的,但是不懂原理,很难讲说深入了,就像不看源码,很难说自己懂这个框架了。
这里补充介绍下我相关B站视频系列的一个很大的优势就是数学、AI计算理论以及代码的三位一体(当然受限于我自己的能力),可能没那么高的高度,但我追求的一种直观和实用,易理解,肯定是有的,是别的那里可能不是那么具备的。
而且我的 B 站的系列都是自然生长出来的,持续不断地在解决我工作学习中的问题,自己想明白之后才做的视频,它至少对于我而言,一定是必要的,甚至是重要的,而不像课本和教材一样,刻意地追求全面。
modern AI for beginners
- 目前暂定的路线主要有如下两个分支,这两个分支统一来说都属于 Generative AI(生成式AI,也是某种形式的大一统),这是我对现代式 ai 的最直白最浅显的理解,然后在 multi modality 处汇合;
- transformer based LLMs
- diffusion models
- multi modality
- 似乎目前越来越强的一个趋势,现代式人工智能越来越演变为一种复杂的大数据、深度学习为核心的复杂计算机科学系统工程的艺术;
- 对一个人全面性的要求越来越高,但要分清主线和支线,支线仅是工具辅助支撑而已;
-
技术栈上主要是围绕 PyTorch 展开,如下我的 B 站系列(可能是最早稳定的一个)
- 经典神经网络模型拓扑结构(pytorch)
-
数学基础,如下我的 B 站系列
- 深度学习的数学基础
- 目前我对数学基础的理解,
-
(多元函数,multivariabel)微积分
- 多元函数微分通向矩阵,jacobian & hessian matrix;
-
矩阵分析;比较直观简单,拿来即用;
- 矩阵矢量乘法,矩阵求逆;
- 矩阵分解:奇异值分解(SVD);
- 矩阵微分:
- $(x^TAx)'=2Ax$
-
数值优化方法;(这两块(优化和矩阵)的工具都比较成熟,大体了解下 solver 即可)
- 对应torch 中的 optimizer,主要是基于数值优化的 gradient-based 的方法
-
主要是概率与数理统计,贝叶斯(我觉得这部分的内容对我来说反而是困难的,因为比较抽象,需要较多的推导,)
- 先验(prior),似然(likelihood),后验(posterior);
-
-
深度学习
-
Dive into Deep Learning
- 如果没有路径依赖果断选择 pytorch 版;
-
Dive into Deep Learning
主要是我在 B 站的三个系列
-
BERT、T5、GPT
- 语言模型的基础
- 一个练习项目:Neural Network: Zero to hearo - Andrej Karpathy 制作的系列视频, 带你从 0 开始构造自己的 nanoGPT
-
personal chatgpt
- 大语言模型的全新范式
-
pytorch distributed
- 多级多卡分布式的基础
软件工程是复杂性管理的艺术; 但显然对于现代式人工智能而言,软件工程是工具是手段,而非目标;
-
强化学习篇(DRL: Deep Reinforcement Learning):李宏毅老师有优先于王树森老师(李宏毅老师符号公式的出发点更主流,王树森老师的推导也很基础,可以辅助帮助理解)
-
Practical Deep Learning for Coders - Jeremy Howard (Kaggel 创始人) 制作的系列课程, 用自顶向下的方式, 从使用预训练模型开始深入到原理, 适合有软件开发经验的人入门
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