Learn how to develop, deploy and iterate on production-grade ML applications.
Repository Index
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💫 Industrial-strength Natural Language Processing (NLP) in Python
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
📺 Discover the latest machine learning / AI courses on YouTube.
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
Topic Modelling for Humans
A curated list of awesome big data frameworks, ressources and other awesomeness.
Low-code framework for building custom LLMs, neural networks, and other AI models
PyMuPDF is a high performance Python library for data extraction, analysis, conversion & manipulation of PDF (and other) documents.
Fast and Accurate ML in 3 Lines of Code
[SIGMOD'27] Easy Data Preparation with latest LLMs-based Operators and Pipelines.
🌊 Online machine learning in Python
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
Python Stream Processing
Python Streaming DataFrames for Kafka
MLBox is a powerful Automated Machine Learning python library.
GRASS - free and open-source geospatial processing engine
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
A pure Python implementation of Apache Spark's RDD and DStream interfaces.
Materials for GWU DNSC 6279 and DNSC 6290.
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
Python package implementing ML feature engineering and pre-processing for polars or pandas dataframes.
Code repository for The Automation Ahead series, showcasing practical examples for GenAI-driven automation in investments. Each installment dives into specific tools, techniques, and use cases to help professionals assess and apply GenAI effectively.