Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. It provides high-level APIs in Java, Scala, Python and R, and supports Spark SQL, pandas API on Spark, MLlib, GraphX and Structured Streaming. The documentation covers downloads, the shell and client applications.
google/BIG-bench
BIG-bench is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. It includes more than 200 tasks, summarized by keyword and task name. The repository was archived by its owner and is now read-only.
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- more than 200 tasks included in BIG-bench are summarized by keyword Source: github.com
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0Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. It provides high-level APIs in Java, Scala, Python and R, and supports Spark SQL, pandas API on Spark, MLlib, GraphX and Structured Streaming. The documentation covers downloads, the shell and client applications.
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0Apache OpenNLP is a machine learning based toolkit for the processing of natural language text. It supports tasks such as sentence segmentation, tokenization, lemmatization, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution. The project is developed by volunteers and welcomes contributions, from small documentation fixes to new components.
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0Licensing is neither feasible nor effective for addressing AI risks provides machine-learning models, research, training resources, or evaluation…
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0A portfolio page for Greg (Grzegorz) Surma that lists side projects related to machine learning, computer vision, self-driving cars and iOS development. It indexes apps and experiments, including AI avatars, image filters, depth estimators, a CNN explainer and games, with the tools and platforms used for each.
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0OpenFace is a free and open source face recognition implementation with deep neural networks, written in Python and Torch and based on the CVPR 2015 FaceNet paper. It detects faces, aligns them, and embeds faces on a 128-dimensional unit hypersphere.
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Rebyte is an execution layer for enterprise agents. It runs agents in the cloud with durable execution, tools such as browsers, files, and compute, and policy controls over models, skills, MCP servers, and network access. Execution traces record model steps, tool calls, errors, tokens, and cost. The Agent API is compatible with the official OpenAI client and is described as model agnostic.
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