Repository Index

The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

• 30-days-of-python
• python
• flask
• github

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

• python
• machine-learning
• deep-learning
• data-science

Data Apps & Dashboards for Python. No JavaScript Required.

• dash
• plotly
• data-visualization
• data-science

阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构

• quant
• trade
• machine-learning
• bitcoin

Open Machine Learning Course

• machine-learning
• data-analysis
• data-science
• pandas

Visual analysis and diagnostic tools to facilitate machine learning model selection.

• machine-learning
• visual-analysis
• model-selection
• visualization

An intuitive library to add plotting functionality to scikit-learn objects.

• scikit-learn
• visualization
• machine-learning
• data-science

:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions

• adaptive-learning
• machine-learning
• python
• live-plots

A Unix, Windows, Raspberry Pi Object Speed Camera using python3, opencv, video streaming, motion tracking. Includes a Standalone Web Server , Image Search using opencv template match and a whiptail Admin Menu Interface Includes picam and webcam Plugins for motion track security camera configuration including rclone sync scripts.

• motion-tracking
• opencv
• rpi-camera
• speed-cam

Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

• pandas
• pandas-summary
• dataframes
• data-science

Fish Audio Drama 3 text-to-speech on the TensorFold native CUDA engine for DGX Spark (GB10): recipe, engine patches, benchmarks. Built with Fish Audio.

A lightweight Python package for teaching computational methods through art history. Extract and analyze artist-specific works from WikiArt with built-in visualizations. Designed for digital humanities and computational design courses. Features genre/style analysis, matplotlib integration, and pedagogical clarity over algorithmic complexity.

• art-history
• color-analysis
• color-extraction
• color-palette

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.