Ingenjoy

Ingenjoy's Stars

• sjchoi86/yet-another-mujoco-tutorial-v3

  Language:Jupyter Notebook7513

• MichaelBeechan/VO-SLAM-Review

  SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects

  22754

• sjchoi86/yet-another-rl-tutorial

  Language:Jupyter Notebook6215

• seungeunrho/RLfrombasics

  provides all the codes from the book "RL from basics(바닥부터 배우는 강화학습)"

  Language:Python14875

• UMich-CURLY-teaching/UMich-ROB-530-public

  UMich 500-Level Mobile Robotics Course

  Language:HTML677122

• byu-magicc/lie_groups

  Implementation-focused introduction to Lie groups for roboticists

  Language:Jupyter Notebook216

• sjchoi86/dl_tutorials

  Deep learning tutorials (2nd ed.)

  1.5k398

• j-marple-dev/python_template

  This repository is base project template for python

  Language:Shell43

• j-marple-dev/AYolov2

  Language:Python15615

• convex-optimization-for-all/convex-optimization-for-all.github.io

  모두를 위한 컨백스 최적화

  Language:CSS16423

• choco9966/Semantic-Segmentation-Review

  Semantic Segmentation Paper를 review하고 해당 코드를 구현하는 repository 입니다.

  Language:Jupyter Notebook2811

• cvg/Hierarchical-Localization

  Visual localization made easy with hloc

  Language:Python3.3k611

• JeiKeiLim/kindle

  Making a PyTorch model easier than ever!

  Language:Python787

• cvxgrp/cvx_short_course

  Materials for a short course on convex optimization.

  Language:Jupyter Notebook334115

• changh95/visual-slam-roadmap

  Roadmap to become a Visual-SLAM developer in 2023

  1.4k144

• Cartucho/mAP

  mean Average Precision - This code evaluates the performance of your neural net for object recognition.

  Language:Python2.9k913

• j-marple-dev/model_compression

  PyTorch Model Compression

  Language:Python23025

• mortendahl/awesome-ppml

  A curated list of resources for privacy-preserving machine learning

  14928

• tbmoon/kalman_filter

  Kalman Filter in Python (파이썬으로 구현하는 칼만 필터)

  Language:Jupyter Notebook15468

• Ingenjoy/SINDY-MPC

  1

• eurika-kaiser/KRONIC

  Koopman Reduced-Order Nonlinear Identification and Control

  Language:MATLAB8532

• MrSyee/pg-is-all-you-need

  Policy Gradient is all you need! A step-by-step tutorial for well-known PG methods.

  Language:Jupyter Notebook881120

• YeungRepo/darpa-sd2

  Language:Jupyter Notebook57

• medipixel/rl_algorithms

  Structural implementation of RL key algorithms

  Language:Python51063

• decrypto-org/blockchain-papers

  A curated list of academic blockchain-related papers

  2.5k561

• golbin/g-coin

  A simple implementation of Blockchain for understanding easily

  Language:Python9427

• JWarmenhoven/ISLR-python

  An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

  Language:Jupyter Notebook4.3k2.4k

• Curt-Park/reinforcement_learning_an_introduction

  Summary (in Korean) and python implementation of 'Reinforcement Learning: An Introduction' written by Sutton & Barto

  Language:Jupyter Notebook5612

• Curt-Park/cs231n_assignments

  [Assignments] CS231N: Convolutional Neural Networks for Visual Recognition (2016 & 2017)

  Language:Jupyter Notebook4610

• metamath1/ml-simple-works

  Language:Jupyter Notebook4114