Machine Learning
Showing 36 of 1845 projects
A Torch7 package for manifold learning and dimensionality reduction, including LLE and t-SNE embeddings.
A JAX transform that implements LoRA (Low-Rank Adaptation) for efficient fine-tuning of large models with minimal memory overhead.
A simple C++ library for multi-armed bandit simulations with multiple policy implementations.
A Python library for easy access, management, and processing of audio datasets, particularly for machine learning tasks.
An in-memory machine learning library for Scala with a scikit-learn-like API, built on Breeze for parallel and distributed systems.
Course materials for UCLA's STATS 418 - Tools in Data Science covering R packages, machine learning libraries, databases, and reproducibility tools.
A compact spiking neural network library built on JAX and Haiku, offering high-performance training via surrogate gradient descent and neuroevolution.
A Clojure wrapper for the Encog machine learning framework, specializing in neural network construction and training.
A lightweight Python library for building reproducible machine learning pipelines with minimal interface constraints.
A library for machine-to-machine interaction with the Coq proof assistant, providing serialization of Coq's internal datatypes to JSON or S-expressions.
A benchmark suite of protein sequence tasks to evaluate machine learning models for protein design.
A Ruby wrapper for Latent Dirichlet Allocation (LDA) that clusters documents into topics with native, Rust, and pure Ruby backends.
A machine learning library for Clojure built on top of Weka, providing filters, classifiers, regression, and clustering algorithms.
A Java framework for developing statistical natural language processing (NLP) components on Apache UIMA.
A machine learning algorithm for accurate, energy-efficient outdoor positioning using 5G mmWave beamformed fingerprints.
A GitHub template for automating machine learning workflows on Azure using GitHub Actions.
A symbolic programming library built on JAX for concise, explicit, and optimized machine learning computations.
A lightweight Python library for explicit, type-checked function configuration via a centralized Python file.
Example code and materials demonstrating practical applications of SAS machine learning techniques.
Tutorial materials for the 2012 IPAM Graduate Summer School on Deep Learning and Feature Learning using Theano and Torch.
Example code and materials demonstrating practical applications of SAS machine learning techniques.
A Python framework for accessing wind data sources and performing renewable energy forecasting and prediction.
A PHP framework for building complex recommendation engines on top of Neo4j graph databases.
A machine learning project comparing topological and statistical feature extraction for classifying human activities from smartphone and smartwatch sensor data.
An automated workflow for generating and storing DFT calculations for organic molecules.
A scikit-learn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.
A benchmark dataset with 3.2 million malicious and benign files across 6 file types for evaluating malware classifiers.
A lightweight feedforward neural network with resilient backpropagation (Rprop), implemented in pure Ruby with no external dependencies.
A deep learning tool for automatic axon and myelin segmentation from microscopy images using convolutional neural networks.
A collection of neuroevolution experiments for reinforcement learning control problems using unsupervised learning feature extractors.
A Python library implementing fairness-aware machine learning algorithms for measuring and mitigating discrimination in predictive models.
A fast, sklearn-like feature processing library for Go that generates optimized transformers from struct tags.
A hands-on workshop introducing deep learning concepts with practical examples using neural networks, CNNs, RNNs, and autoencoders.
A Fiji plugin for pixel-based image segmentation using Weka machine learning algorithms and image features.
A method for selecting interpretable feature subsets from complex models using mutual information optimization.
A strongly-typed genetic programming framework for Python that makes evolutionary algorithms accessible and fun.
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