Superduper: End-to-end framework for building custom AI applications and agents.
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Simple and Distributed Machine Learning Python Library porting ML algorithms for Spark
Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.
modular quant framework.
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Curated list: Resources for machine learning in Ruby
Build LLM-powered applications in Ruby
An open source DevOps tool from the CNCF for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI Artifact.
Hopsworks - Data-Intensive AI platform with a Feature Store
Rumale is a machine learning library in Ruby
Train and Deploy an ML REST API to predict crypto prices, in 10 steps
Framework and Library for Distributed Online Machine Learning
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
Sharing Updatable Models (SUM) on Blockchain
Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.
CellViT: Vision Transformers for Precise Cell Segmentation and Classification
Ruby language bindings for LIBSVM
Powerful Powerful Machine Learning library with GPU, CPU and WASM backends
Apache cTAKES is a Natural Language Processing (NLP) platform for clinical text.
Curated List of AI, ML and Deep Learning books
TensorFlow.Delphi (TF.Delphi) provides a Delphi(Pascal) Standard binding for TensorFlow. It aims to implement the complete Tensorflow API in Delphi which allows Pascal developers to develop, train and deploy Machine Learning models with the Pascal Delphi
GG.Net Data Visualization
No description provided by the repository host.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.