bauer79's Stars
fossasia/visdom
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.
facebookresearch/detectron2
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
facebookresearch/Kats
Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
marktext/marktext
📝A simple and elegant markdown editor, available for Linux, macOS and Windows.
liyangbit/Python-Knowledge-Handbook
Python Knowledge Handbook by Python数据之道, website: http://liyangbit.com/
MingchaoZhu/InterpretableMLBook
《可解释的机器学习--黑盒模型可解释性理解指南》,该书为《Interpretable Machine Learning》中文版
Autodesk-Forge/forge-viewmodels
View models from listof buckets and objects: This basic C# WebAPI back-end sample implements a basic list of Buckets and Objects with an Autodesk Forge 2-Legged Token
pystra/pystra
Python Structural Reliability Analysis
maziarraissi/PINNs
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
datawhalechina/leedl-tutorial
《李宏毅深度学习教程》(李宏毅老师推荐👍,苹果书🍎),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
mli/paper-reading
深度学习经典、新论文逐段精读
bovender/XLToolbox
Daniel's XL Toolbox is an open-source add-in for Excel(R) that assists with scientific and technical data analysis and visualization.
garrettj403/SciencePlots
Matplotlib styles for scientific plotting
datawhalechina/easy-rl
强化学习中文教程(蘑菇书🍄),在线阅读地址:https://datawhalechina.github.io/easy-rl/
datawhalechina/pumpkin-book
《机器学习》(西瓜书)公式详解
dicengine/dice
Digital Image Correlation Engine (DICe): a stereo DIC application that runs on Mac, Windows, and Linux
mml-book/mml-book.github.io
Companion webpage to the book "Mathematics For Machine Learning"
fengdu78/WZU-machine-learning-course
温州大学《机器学习》课程资料(代码、课件等)
huggingface/pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
Smorodov/Multitarget-tracker
Multiple Object Tracker, Based on Hungarian algorithm + Kalman filter.
rlabbe/Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
rlabbe/filterpy
Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.
amusi/PyTorch-From-Zero-To-One
PyTorch从入门到精通
vrdmr/CS273a-Introduction-to-Machine-Learning
Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".
szcf-weiya/ESL-CN
The Elements of Statistical Learning (ESL)的中文翻译、代码实现及其习题解答。
salabim/salabim
salabim - discrete event simulation
douthwja01/OpenMAS
OpenMAS is an open source multi-agent simulator based in Matlab for the simulation of decentralized intelligent systems defined by arbitrary behaviours and dynamics.
projectmesa/mesa
Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors.
Dod-o/Statistical-Learning-Method_Code
手写实现李航《统计学习方法》书中全部算法
lux-org/lux
Automatically visualize your pandas dataframe via a single print! 📊 💡