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Julia-language backend combined with the Jupyter interactive environment.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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A delightful machine learning tool that allows you to train/fit, test and use models without writing code.
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An open-source visual programming environment for battle-testing prompts to LLMs.
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Toolset for black-box hyperparameter optimisation.
Code for hyperparameter tuning/optimization of machine learning and deep learning algorithms.
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Distributed Asynchronous Hyperparameter Optimization in Python.
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General Automated Machine Learning Framework.
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A very simple wrapper for convenient hyperparameter optimization.
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A service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
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Platform for deploying your Machine Learning to production.
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A framework for elegantly configuring complex applications.
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"building applications with LLMs through composability".
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General Machine Learning library using Numenta’s Cortical Learning Algorithm.
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Library for hyperparameter optimization and black box optimization benchmarks.
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Making large AI models cheaper, faster and more accessible.
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Framework for distributed hyperparameter optimization.
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Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
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Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Framework for building applications with LLMs and Transformers (e.g. agents, semantic search, question-answering).
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
github.com
Run LLM backends, APIs, frontends, and services with one command.