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-tianchi-llm-qa_rag

阿里天池: 2023全球智能汽车AI挑战赛——赛道一：AI大模型检索问答 baseline 80+

100-days-of-ml-code

100-Days-Of-ML-Code中文版

100-days-of-ml-code-1

100 Days of ML Coding

activemq

1.消息队列MQ的项目实战代码

adbench

Official Implement of "ADBench: Anomaly Detection Benchmark".

advanced-deep-learning-with-keras

Advanced Deep Learning with Keras, published by Packt

agriculture-knowledgegraph-data

对知识库Wikidata的爬虫以及数据处理脚本 将三元组关系对齐到语料库的脚本 获取知识图谱数据的脚本

agriculture_knowledgegraph

农业知识图谱(AgriKG)：农业领域的信息检索，命名实体识别，关系抽取，智能问答，辅助决策

ai-challenger-2018-cropdisease

ai-challenger-plant-disease-recognition

AI Challenger -- 农作物病害识别

ai-competition

开源往期获奖竞赛代码

ai-ml

A curated list of machine learning and AI projects.

aiops-anomaly-detection

anomaly-detection

UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.

anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

anomaly-detection-with-autoencoders

Anamoly Detection with Autoencoders - Credit Card Fraud Case

anomalydetectiontimeseriesdata

Anamoly Detection in Time Series data of S&P 500 Stock Price index (of top 500 US companies) using Keras and Tensorflow

anyq

FAQ-based Question Answering System

appliedtimeseriesanalysiswithpython

arima-lstm-hybrid-corrcoef-predict

Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

awesome-aiops

AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star

awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

awesome-ts-anomaly-detection_zh

bar_chart_race

Create animated bar chart races in Python with matplotlib

bearing_detection_by_conv1d

[深度应用]·DC竞赛轴承故障检测开源Baseline（基于Keras1D卷积 val_acc:0.99780）

beginning-anomaly-detection-using-python-based-dl

Source Code for 'Beginning Anomaly Detection Using Python-Based Deep Learning' by Sridhar Alla and Suman Kalyan Adari

bigmartpredmodel

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. The aim is to build a predictive model and find out the sales of each product at a particular store.Using this predictive model the data scientists at BigMart can understand the properties of products and stores which play a key role in increasing sales. The tasks done for this project are : *Data Exploration *Data QAQC *Outlier detection and missing data replacement strategy *Data ETL *Understanding about sales domain *Feature Engineering *Advanced Data Analysis *Regression modeling Techniques *Hybrid modeling Techniques *Specific measure to Evaluate Model ​ The datasets were acquired from : https://s3.amazonaws.com/hackerday.datascience/49/Test_u94Q5KV.csv https://s3.amazonaws.com/hackerday.datascience/49/Train_UWu5bXk.csv https://s3.amazonaws.com/hackerday.datascience/49/SampleSubmission_TmnO39y.csv Software Requirements : Continuum Analytics, Inc software Python 2.7 version with seaborn package was used.

blog

Python机器学习算法技术博客，有原创干货！有code实践！

book3-kg

bookrepo