sunyingjian

AI-in-well-logging

人工智能在石油测井上的应用包括采用机器学习，深度学习等相关方法进行岩性识别与相关测井曲线的回归。The application of artificial intelligence in well logging includes the use of machine learning, deep learning and other related methods for lithology identification and regression of related well logging data.

numpy-

开源的测井数据集 Open source well logging data set 适用于机器学习分析地下储层岩性识别与分类

boosting-and-others

三大boosting算法的工程实现 XGBoost、LightGBM、Catboost原理实现及常见面试问题总结，以及其他理解深刻的机器学习、深度学习文章备份

pylog

基于Python PyQt5实现可视化的 PyLog 自动化测井辅助解释软件

Python-100-Days

Python - 100天从新手到大师

Machine-Learning-From-Scratch

常用机器学习的算法简洁实现

ML_by_my_own_hand

手动自主实现机器学习算法

Baidu-AI-course

飞桨深度学习学院出品的顶会复现营代码

study-tensorflow

学习tensorflow课程的代码

data-structural

my_python_tool

numpy-private

TensorFlow

tutorials-2016

Geophysical Tutorials for 2016 美国油田的数据

YSUthesis

Master thesis template for Yanshan University

catboost-zh-cn.github.io

dl-4-tsc

Deep Learning for Time Series Classification

Facies-Classification-Machine-Learning

FORCE-2020-Lithology

A collection of notebooks exploring ideas for the Force 2020 Lithology Classification Challenge.

Force-2020-Machine-Learning-competition

the results, code and the data for the Force 2020 Machine learning competition after the completion of the competition in October 2020.

jdjd

sunyingjian.github.io

tf-course-youdao-2021

2021年**大学mook tensorflow官方课程部分代码（主要为nlp部分）

TSP_collection

TSP算法全复现：遗传(GA)、粒子群(PSO)、模拟退火(SA)、禁忌搜索(ST)、蚁群算法(ACO)、自自组织神经网络(SOM)