fujianyan Goto Github PK

-

润学全球官方指定GITHUB，整理润学宗旨、纲领、理论和各类润之实例；解决为什么润，润去哪里，怎么润三大问题； 并成为新**人的核心宗教，核心信念。

2024_summer_ai_camp

academicpages.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

ai_logic_resource_notes

Learn about Machine Learning and Artificial Intelligence

annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

auroransl

Aurora prediction by neural symbolic learning

books

My book list

books-1

我读过的书。嘿嘿，分享给你。

cs229-2018-autumn

All notes and materials for the CS229: Machine Learning course by Stanford University

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被60个国家的400所大学用于教学。

deep-learning-interview-book

深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deeplearning

深度学习入门教程, 优秀文章, Deep Learning Tutorial

dict-deep

An Architecture for Action Detection in Videos using Over-Complete Dictionary Learning

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/

https-github.com-django-sample-app

ieee_robotic_club.github.io

interactive_projector

Fourth Year Project 2 Interactive projector implementation

interview

📚 C/C++面试基础知识总结

localization-particle-filter-python

Localize the car in a static map with a particle filter.

machine-learning

:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

pyneuralogic

PyNeuraLogic lets you use Python to create Differentiable Logic Programs

python_for_data_analysis_2nd_chinese_version

《利用Python进行数据分析·第2版》

r-gan

Getting 3D volumetric information for an object is essential in applications ranging from autonomous manufactur- ing to robotic scene perception. In order to get 3D volumetric information for an object, RGB-D sensors are widely used to capture depth information. To reconstruct 3D volumetric infor- mation of an object, this paper designed an extended generative adversary network (GAN) with a recurrent generator. The model can take a single or a sequence of depth scans of an object to reconstruct the 3D volumetric model of the object. In precise, 3D long short-term memory (LSTM) units that are employed in the generator can extract features from the sequence of depth scans in different time steps. The reconstructed results of the proposed model are evaluated by calculating intersection over union (IoU) in both 3D space and 2D projection. The model achieved 77.71% in IoU, 80.08% in hit rate, and 97.45% in accuracy, which outperformed other methods.

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

stanford-cs-221-artificial-intelligence

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

test_projector

universal_robots_ros_tutorials

Tutorials around the Universal Robots ROS (1) ecosystem

wsu_data_science_club