Machine Learning
Showing 36 of 1845 projects
A Scala toolkit for deployable probabilistic modeling using imperatively-defined factor graphs.
A Python library implementing Self-Organizing Maps (SOM) with batch training, PCA initialization, and visualization tools.
Automatically builds high-performance interpretable machine learning models with minimal features using a single line of code.
A Python library for statistical learning with a focus on time-dependent modeling, including point processes and generalized linear models.
A curated list of popular deep learning models for image classification, segmentation, and detection with key performance metrics.
A TypeScript machine learning library for the web and Node.js with a simple, consistent API.
Cleora is a fast, deterministic graph embedding engine that computes all random walks in a single matrix multiplication, requiring no GPUs or negative sampling.
A Flutter plugin providing fast, flexible TensorFlow Lite inference with multi-platform delegate support.
A modular autonomous driving platform for developing and testing AV components on CARLA simulator and real-world vehicles.
An engine for ML/data tracking, visualization, explainability, drift detection, and dashboards, integrated with Polyaxon.
A unified deep learning and reinforcement learning framework supporting multiple backends and hardware platforms.
A guide to applying Test-Driven Development and clean architecture principles when building software from Jupyter notebooks.
A comprehensive PhD dissertation providing an in-depth theoretical and practical analysis of random forests, from algorithmic foundations to interpretability.
A Python package for benchmarking generative models in de novo molecular design.
A Python package providing Bayesian machine learning algorithms with a scikit-learn compatible API.
A TensorFlow library implementing constrained and interpretable lattice-based models with shape constraints like monotonicity and convexity.
A reading comprehension dataset with Wikipedia summaries, full stories, and question-answer pairs for narrative understanding.
An open-source MLOps framework for defining and deploying machine learning and LLM workloads across any cloud infrastructure.
A book teaching practical patterns for building scalable and reliable distributed machine learning systems using Kubernetes, TensorFlow, Kubeflow, and Argo Workflows.
An AutoML framework that generates and customizes machine learning pipelines using declarative JSON-AI syntax.
A Go library implementing word embedding models (Word2Vec, GloVe, LexVec) from scratch with CLI and SDK.
A standalone reimplementation of TensorFlow for Ruby, supporting pure Ruby and OpenCL backends for machine learning.
Ruby gem providing bindings to FANN (Fast Artificial Neural Network) for building and training neural networks.
A fast C++ GPU implementation of Convolutional Neural Networks with multi-GPU support.
Winning solution for the Galaxy Challenge on Kaggle, using convolutional neural networks to classify galaxy morphologies.
A collection of BERT-like transformer models pre-trained on chemical SMILES data for drug design and property prediction.
A Spark Streaming library for mining big data streams with incremental learning algorithms.
An automated cell type annotation tool for single-cell RNA-seq data using logistic regression classifiers.
A JavaScript library for reinforcement learning using Markov Decision Processes, implemented in C++ for performance.
A PostgreSQL extension that uses machine learning to improve query cardinality estimation and optimize execution plans.
A TensorFlow implementation of the neural style transfer algorithm that applies artistic styles to images.
A scikit-learn compatible classifier that produces human-interpretable decision rules instead of black box models.
An open-source benchmark toolkit for Natural Language Generation in spoken dialogue systems, featuring multiple RNN-based models and datasets.
A high-performance matrix library for Elixir/Erlang with C and CBLAS backend, optimized for speed and large-scale operations.
A lightweight Python decision tree framework supporting ID3, C4.5, CART, CHAID, regression trees, gradient boosting, random forest, and AdaBoost with categorical feature support.
Classify images offline on iOS using Watson Visual Recognition trained models and Apple's Core ML framework.
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