人工智能
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人工智能
整理一些人工智能相关知识
分类
- 人工智能(AI)
- 机器学习(ML)
- 神经网络(NN)
- 深度学习(DL)
应用
- 传统任务:回归、分类、聚类
- 深度学习:机器视觉、自然语言
机器视觉
图像分类、物体检测、图像分割等等
自然语言
语音识别、机器翻译、自动摘要、观点提取、文本分类、问题回答等等
回归算法
线性回归算法(Linear Regression)
多项式回归算法(Polynomial Regression)
分类算法
邻近算法(KNN)
支持向量机(SVM)
隐马尔可夫模型(HMM)
随机森林算法(Random Forest)
逻辑回归算法(Logistic Regression)
Softmax逻辑回归算法(Softmax Regression)
聚类算法
K均值聚类算法(K-Means)
基于密度的聚类算法(DBSCAN)
神经网络算法 & 深度学习算法 & 生成网络算法
人工神经网络(ANN)
卷积神经网络(CNN)
机器视觉
视觉几何组(VGG)
残差神经网络(ResNet)
循环神经网络(RNN)
自然语言
门控循环单元(GRU)
长短时记忆网络(LSTM)
生成对抗网络(GAN)
- https://github.com/podgorskiy/ALAE
- https://github.com/junyanz/CycleGAN
- https://github.com/TencentARC/GFPGAN
- https://github.com/eladrich/pixel2style2pixel
- https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
循环生成对抗网络(CycleGAN)
基于风格的对抗网络(StyleGAN)
mamba
- https://github.com/state-spaces/mamba
Transformer
T5/GPT/BERT/自注意力机制(Self-Attention)
- https://github.com/ollama/ollama
- https://github.com/huginn/huginn
- https://github.com/lencx/ChatGPT
- https://github.com/xtekky/gpt4free
- https://github.com/ggerganov/llama.cpp
- https://github.com/ggerganov/whisper.cpp
- https://github.com/huggingface/transformers
gemma
llama
大语言模型(LLM)
- https://github.com/THUDM/ChatGLM3
- https://github.com/THUDM/ChatGLM-6B
- https://github.com/THUDM/ChatGLM2-6B
- https://github.com/ymcui/Chinese-LLaMA-Alpaca
- https://github.com/ymcui/Chinese-LLaMA-Alpaca-2
扩散模型(Diffusion)
- https://github.com/huggingface/peft
- https://github.com/huggingface/diffusers
- https://github.com/leejet/stable-diffusion.cpp
- https://github.com/AUTOMATIC1111/stable-diffusion-webui
Embedding
- https://github.com/supabase/supabase
- https://github.com/chroma-core/chroma
- https://github.com/Embedding/Chinese-Word-Vectors
AI Agent
- https://github.com/geekan/MetaGPT
- https://github.com/reworkd/AgentGPT
- https://github.com/langchain-ai/langchain
- https://github.com/Significant-Gravitas/AutoGPT
- https://github.com/PlexPt/awesome-chatgpt-prompts-zh
- https://github.com/chatchat-space/Langchain-Chatchat
模型量化
模型微调
模型精调
激活函数
优化算法
学习资料
- https://zh-v2.d2l.ai/
- https://github.com/hankcs/HanLP
- https://github.com/d2l-ai/d2l-zh
- https://github.com/Ewenwan/MVision
- https://github.com/NLP-LOVE/ML-NLP
- https://github.com/tangyudi/Ai-Learn
- https://github.com/apachecn/ailearning
- https://github.com/MorvanZhou/tutorials
- https://github.com/graykode/nlp-tutorial
- https://github.com/fighting41love/funNLP
- https://dataxujing.github.io/ASR-paper/#/
- https://dataxujing.github.io/NLP-paper/#/
- https://dataxujing.github.io/TTS-paper/#/
- https://dataxujing.github.io/CNN-paper2/#/
- https://dataxujing.github.io/AIGC-paper/#/
- https://github.com/Mikoto10032/DeepLearning
- https://github.com/microsoft/AI-For-Beginners
- https://github.com/microsoft/ML-For-Beginners
- https://github.com/DA-southampton/NLP_ability
- https://dataxujing.github.io/libtorch_tutorials/
- https://github.com/chenzomi12/DeepLearningSystem
- https://github.com/brightmart/nlp_chinese_corpus
- https://github.com/datawhalechina/leedl-tutorial
- https://scikit-learn.org/stable/modules/classes.html
- https://github.com/scutan90/DeepLearning-500-questions
- https://github.com/microsoft/generative-ai-for-beginners
- https://github.com/labmlai/annotated_deep_learning_paper_implementations