12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

12 Weeks, 24 Lessons, AI for All!

Code for the paper "Causal Transformer for Estimating Counterfactual Outcomes"

Interact with your SQL database, Natural Language to SQL using LLMs

Understanding Deep Learning - Simon J.D. Prince

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

Google Research

📈 PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series 📈

📦 A Python package for online changepoint detection, implementing state-of-the-art algorithms and a novel approach based on neural networks.

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

A collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain

TensorFlow implementation of TCAN model for multivariate time series forecasting with sparse attention mechanisms.

Change-point detection using neural networks

Multidisciplinary Research and Education on Big Data + High-Performance Computing + Atmospheric Sciences at UMBC

Best Practices on Recommendation Systems

Edge Inference in Browser with Transformer NLP model

💡 Build AI-powered semantic search applications

DeText: A Deep Neural Text Understanding Framework for Ranking and Classification Tasks

Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, B…

re_data - fix data issues before your users & CEO would discover them 😊

📄 🤖 AI-powered literature discovery and review engine for medical/scientific papers

Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris

Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series

GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba

A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.

A data orchestrator for machine learning, analytics, and ETL.

Kubernetes-native workflow automation platform for complex, mission-critical data and ML processes at scale. It has been battle-tested at Lyft, Spotify, Freenome, and others and is truly open-source.

Making Docker and Kubernetes management easy.

🔥 🔥 The Open Source Airtable alternative.