caoknew's starred repositories
AI-interview-cards
最完整的AI算法面试题目仓库，1000道，25个类目
Llama-Chinese
Llama中文社区，Llama3在线体验和微调模型已开放，实时汇总最新Llama3学习资料，已将所有代码更新适配Llama3，构建最好的中文Llama大模型，完全开源可商用
LLM-Tuning
Tuning LLMs with no tears💦; Sample Design Engineering (SDE) for more efficient downstream-tuning.
Awesome-Diffusion-Models
A collection of resources and papers on Diffusion Models
openai-cookbook
Examples and guides for using the OpenAI API
pytorch_influence_functions
This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.
Chinese-CLIP
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
Chinese-BERT-wwm
Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）
EduCDM
The Model Zoo of Cognitive Diagnosis Models, including classic Item Response Ranking (IRT), Multidimensional Item Response Ranking (MIRT), Deterministic Input, Noisy "And" model(DINA), and advanced Fuzzy Cognitive Diagnosis Framework (FuzzyCDF), Neural Cognitive Diagnosis Model (NCDM) and Item Response Ranking framework (IRR).
dlwpt-code
Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.
BilibiliVideoDownload
Cross-platform download bilibili video desktop software, support windows, macOS, Linux
PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
bayes-by-backprop
PyTorch implementation of "Weight Uncertainty in Neural Networks"
recommendation_model
练习下用pytorch来复现下经典的推荐系统模型, 如MF, FM, DeepConn, MMOE, PLE, DeepFM, NFM, DCN, AFM, AutoInt, ONN, FiBiNET, DCN-v2, AFN, DCAP等
AI-Expert-Roadmap
Roadmap to becoming an Artificial Intelligence Expert in 2022
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
CrowdLayer
A neural network layer that enables training of deep neural networks directly from crowdsourced labels (e.g. from Amazon Mechanical Turk) or, more generally, labels from multiple annotators with different biases and levels of expertise.