Directory
Published directory entries matching your search.
LAION, the Large-scale Artificial Intelligence Open Network, is a non-profit organization that provides datasets, tools and models to support open machine learning research. Its offerings include the LAION-400M dataset of 400 million English image-text pairs, LAION-5B of 5.85 billion multilingual CLIP-filtered pairs, and the CLIP H/14 vision transformer model.
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.
A repository of material for the exercise classes of a Big Data Analytics master course. Hands-on sessions cover Spark and its machine learning library, Map/Reduce implementations of linear regression and alternating least squares, streaming analytics with Recursive Least Squares, and image classification with convolutional neural networks. Material is provided as Jupyter notebooks.
NASGym is a proof-of-concept OpenAI Gym environment for Neural Architecture Search. It implements reinforcement learning steps for NAS and encodes networks using the Neural Structure Code from BlockQNN. A network is modeled as a list of NSCs, an action adds a layer, and the reward is accuracy after early-stop training. CIFAR-10 is the default dataset. The code and documentation may contain minor bugs.
NDScala is a Scala 3 library for N-dimensional and multi-dimensional arrays (tensors), described as comparable to NumPy ndarray or PyTorch Tensor, with type safety over shapes, array and axis labels and numeric data types. The repository includes materials on shape-safe neural network training and an ONNXScala component.
NeuroNER is a program that performs named-entity recognition using neural networks. It relies on Python 3 and TensorFlow 1.0+, optionally uses the BRAT annotation tool, and provides installation instructions, sample datasets and pretrained models.
An offline AI application for phones and Macs that supports chat, image generation, vision, Whisper speech-to-text, tool calling and local-network servers. It runs on CPU, GPU or NPU, requires no account or API key, and keeps data on the device. A paid Pro tier adds voice mode, custom personas and draft-and-approve actions.
Pattern is a web mining module for Python with tools for data mining, natural language processing, machine learning, and network analysis and visualization. The repository is archived and no longer maintained, receiving no further updates, bug fixes or security patches.
A Python implementation of Restricted Boltzmann Machines. It lets users initialize an RBM with a chosen number of visible and hidden units, train it on a matrix of training examples, run visible or hidden data, and let the network run freely. It is released under an MIT license.
Vega is an AutoML algorithm tool chain developed by Noah's Ark Laboratory. It covers hyperparameter optimization, data augmentation, network architecture search, model compression and fully train functions, and supports PyTorch, TensorFlow and MindSpore backends.
WebNN is a web standard that lets web apps and frameworks accelerate deep neural networks using on-device hardware such as GPUs, CPUs, or purpose-built AI accelerators. The introduction states that in-browser inference supports local media sources, keeps user data on-device, and needs no network after initial asset caching.
