Stars
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.
Python programs, usually short, of considerable difficulty, to perfect particular skills.
💿 Free software that works great, and also happens to be open-source Python.
✔（已完结）超级全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】【大飞 大模型Agent】
📡 Simple and ready-to-use tutorials for TensorFlow
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接
links to conference publications in graph-based deep learning
cluster data collected from production clusters in Alibaba for cluster management research
Bayesian learning and inference for state space models
Python class for generation and parameter estimation of multivariate Hawkes processes
Implemented Machine Learning Algorithms in Hyperbolic Geometry (MDS, K-Means, Support vector machines, etc.)
Crawl & visualize ICLR papers and reviews.
Code for the intrinsic dimensionality estimate of data representations
Code and Data for CIKM Paper Feature Driven and Point Process Approaches for Popularity Prediction
COVID-19 infectious forecasting using SEIR model and R0 estimation
Public Jupyter notebooks from Fugro Roames
SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations
Implementation of Nonparametric Hamiltonian Monte Carlo
复杂网络研究资源整理和基础知识学习
High Reconstructability of Degree-Heterogeneous Networks