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Published directory entries matching your search.
H2O is an open-source, distributed, fast, and scalable machine learning platform hosted on GitHub by the h2oai organization. It supports a wide range of machine learning methods including deep learning, gradient boosting, random forest, generalized linear modeling, clustering, dimensionality reduction, rule fitting, support vector machines, stacked ensembles, and automated machine learning.
imutils is a Python package of convenience functions that make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV. It works with Python 2.7 and Python 3 and is pip-installable given NumPy, SciPy, Matplotlib and OpenCV. A find_function method searches function names across modules.
A reference page in the Clojure documentation covering Java interop, including class access, member access, the dot special form, implementing interfaces and extending classes, arrays, vararg methods and type hints.
A Clojure wrapper for deeplearning4j with added syntactic sugar, aiming to provide a declarative API for machine learning on the JVM. It translates Clojure keywords into Java method calls, making many deeplearning4j configuration options accessible. The project is a work in progress, and configurations have been tested on basic feedforward nets, RNNs and CNNs.
A curated GitHub list of resources on audio-driven talking face generation. It organizes research papers and projects, including encoder-decoder and StyleGAN-based methods, pose-controllable and lip-sync work, and 3D model-based approaches.
Keenious is an AI-assisted tool for exploring published academic research. It searches about 177M publications, groups results into research areas on a map, and explains unfamiliar terms, methods, and statistics beside the paper. It also translates studies published in other languages. The source states it does not write essays, paragraphs, or assignment outlines.
This webpage provides a curated list of large language model examples, alongside insights on business adoption, language model training, and influential models. It includes comparative analysis of leading LLMs such as OpenAI’s GPT-5, Claude 4.6, Gemini, DeepSeek-R1, Qwen, Llama 4, and xAI Grok-4 and Grok-4.1.
Lasagne is a lightweight library for building and training neural networks in Theano. It supports feed-forward networks such as convolutional neural networks, recurrent networks including LSTM, multiple inputs and outputs, and optimization methods including Nesterov momentum, RMSprop and ADAM. Cost functions are freely definable and gradients derive from Theano's symbolic differentiation.
The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sum-product networks and arithmetic circuits. Each algorithm is implemented as a command-line program with consistent options and file formats, and all methods are written in OCaml.
Little Ball of Fur is a graph sampling extension library for Python and a graph sampling extension library for NetworKit and NetworkX. It consists of methods that can sample from graph structured data, covering vertex, edge, and exploration sampling techniques with a unified application public interface.
MIT 6.S191 is MIT's introductory program on deep learning methods, with applications in vision, robotics, medicine, language, game play, art and more. It is described as a high-intensity bootcamp teaching deep learning fundamentals, with lectures, software labs and a final project proposal competition. Prerequisites assume calculus and linear algebra.
ML-From-Scratch is a GitHub repository that provides bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility, aiming to cover methods from linear regression to deep learning.
The Data Science MSc is a postgraduate taught programme at the University of Edinburgh. It covers the computational principles, methods and systems for extracting and structuring knowledge from data, and the application of those principles. The programme notes that large data sets are generated by almost every activity in science, society and commerce.
Methods for dimensionality reduction.
NExT-Chat is a large multimodal model trained using the pixel2emb method, in which the model outputs location embeddings decoded by different decoders. This allows bounding boxes and masks in multimodal conversations and supports tasks such as visual grounding, region caption and grounded reasoning.
NightCafe Creator is an AI Art Generator app with multiple methods of AI art generation.
NMF.jl is a Julia package for non-negative matrix factorization, which factorizes a non-negative matrix into two lower-rank non-negative matrices. The nnmf function runs initialization and optimization in one call. Implemented methods include multiplicative update, projected alternating least squares, projected gradient and coordinate descent, with random and NNDSVD initialization.
numpy-ML is a GitHub-hosted library that implements various machine learning algorithms using only NumPy. The project emphasizes rapid experimentation and can be used as a package. It covers a range of ML methods and includes documentation for users. The repository is open source and welcomes contributions.
Orange is an open-source machine learning and data visualization toolbox. It offers visual programming in which widgets are placed on a canvas and connected to load datasets and analyze data. It supports interactive visualization methods including box plots, scatter plots, decision trees, hierarchical clustering, heatmaps, MDS, t-SNE and linear projections. Extensions add text mining, network analysis and other specialized capabilities.
Paintings by Style on WikiArt is a webpage that catalogs artworks by art style, with styles ranging from Gothic to Impressionism. It defines art style as the distinctive visual elements, techniques, and methods of an artwork, which typically correspond to an art movement or the school or group its creator is associated with.
PEFT is a library of parameter-efficient fine-tuning methods from Hugging Face. It adapts large pretrained models by fine-tuning only a small number of extra parameters instead of all model parameters, which decreases computational and storage costs. It is integrated with Transformers, Diffusers and Accelerate.
Prompt Engineering Lil'Log is a blog post focused on prompt engineering for autoregressive language models. It covers methods to steer large language model behavior without updating model weights, including zero-shot prompting, few-shot prompting, and chain-of-thought prompting.
PyStanfordDependencies is a Python interface for converting Penn Treebank trees to Stanford Dependencies and Universal Dependencies. It offers a get_instance() function with subprocess or jpype backends, and convert_tree() or convert_trees() methods returning Token objects. Visualization options include asciitree, Graphviz dotgraphs, and interactive xdot windows. A Stanford CoreNLP or Parser jar file can be supplied, or downloaded automatically.
PyTorch Geometric Temporal is a temporal extension library for PyTorch Geometric. It contains dynamic and temporal geometric deep learning, embedding and spatio-temporal regression methods from published research papers. It includes a dataset loader, train-test splitter and temporal snapshot iterator for dynamic and temporal graphs, with GPU support and benchmark datasets.
