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Illustration of transfer learning. Transfer learning ( TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
LightGBM. LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [4] [5] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance ...
DVC is a free and open-source, platform-agnostic version system for data, machine learning models, and experiments. [1] It is designed to make ML models shareable, experiments reproducible, [2] and to track versions of models, data, and pipelines. [3] [4] [5] DVC works on top of Git repositories [6] and cloud storage.
GitHub (/ ˈ ɡ ɪ t h ʌ b /) is a developer platform that allows developers to create, store, manage and share their code.It uses Git software, providing the distributed version control of Git plus access control, bug tracking, software feature requests, task management, continuous integration, and wikis for every project.
GitHub Copilot. GitHub Copilot is a code completion tool developed by GitHub and OpenAI that assists users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs) by autocompleting code. [1] Currently available by subscription to individual developers and to businesses, the generative artificial ...
Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. [3] It provides LuaJIT interfaces to deep learning algorithms implemented in C. It was created by the Idiap Research Institute at EPFL. Torch development moved in 2017 to PyTorch, a port of the library to Python.
Domain adaptation is the ability to apply an algorithm trained in one or more "source domains" to a different (but related) "target domain". Domain adaptation is a subcategory of transfer learning. In domain adaptation, the source and target domains all have the same feature space (but different distributions); in contrast, transfer learning ...
StyleGAN. An image generated using StyleGAN that looks like a portrait of a young woman. This image was generated by an artificial neural network based on an analysis of a large number of photographs. StyleGAN is a generative adversarial network (GAN) introduced by Nvidia researchers in December 2018, [1] and made source available in February ...