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Auto ViML automatically builds multiple machine learning models with a single line of code. It was created by Ram Seshadri, and collaborators are welcome with permission. The tool is available on GitHub.
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Backprop is a Python library, installable via PyPi, that enables use, finetuning and deployment of machine learning models. It provides task interfaces for conversational question answering, text classification in over 100 languages, image classification, summarisation, emotion detection and text generation, and models can be finetuned with a single line of code.
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Brain.js is a GitHub-hosted JavaScript library for building and running neural networks. It provides GPU-accelerated neural network functionality compatible with both web browsers and Node.js environments, allowing users to implement machine learning models using JavaScript across different runtime platforms.
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dataframe-go is a public software repository hosted on GitHub by rocketlaunchr. It provides DataFrame functionality for the Go programming language, designed to support statistics, machine learning, and data manipulation or exploration tasks.
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DIGITS, the Deep Learning GPU Training System, is a webapp for training deep learning models. The repository was archived by its owner on Jan 7, 2025 and is now read-only. NVIDIA states it is no longer adding features, fixing bugs, or supporting the software. Users can continue to use it if it meets their needs.
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golearn is an open-source GitHub repository maintained by sjwhitworth. It provides machine learning capabilities for the Go programming language. The repository includes installation instructions, getting started guides, example use cases, and full project documentation.
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Gymnasium is an open source Python library for developing and comparing reinforcement learning algorithms. It provides a standard API between learning algorithms and environments, plus reference environments including Classic Control, Box2D, Toy Text, MuJoCo and Atari. It is a fork of OpenAI's Gym library maintained by its maintainers.
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H2O Sparkling Water is an open-source GitHub repository maintained by the h2oai organization. It provides H2O machine learning functionality that can run inside Apache Spark clusters. The repository includes getting started guides, user documentation, binary download options, and resources for testing the tool.
IPython notebooks from Data School's video tutorials on scikit-learn.
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Open-source Java scientific computing library for JVM and Android platforms, with SciPy-like APIs for use in machine learning and data science workflows. It provides functionality for signal processing, FFT, filters, PSD, and DCT operations.
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A Clojure wrapper for deeplearning4j with added syntactic sugar, aiming to provide a declarative API for machine learning on the JVM. It translates Clojure keywords into Java method calls, making many deeplearning4j configuration options accessible. The project is a work in progress, and configurations have been tested on basic feedforward nets, RNNs and CNNs.
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KotlinDL is a high-level deep learning API written in Kotlin and inspired by Keras. It uses TensorFlow Java API and ONNX Runtime API for Java, and offers APIs for training models from scratch, importing Keras and ONNX models for inference, and transfer learning.
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Magenta is a research project exploring machine learning in art and music creation, developing deep learning and reinforcement learning algorithms for generating songs, images, and drawings. It also explores tools letting artists and musicians extend their processes with these models. The repository is archived and read-only.
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Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
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modAL is a modular active learning framework designed for use with the Python programming language. The project includes introductory documentation, overviews of active learning concepts, usage examples, and quickstart guides. It is hosted as an open source repository on GitHub.
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A parallel deep learning framework hosted on GitHub. The project provides a framework for building and running deep learning workflows with parallel processing support, and includes documentation for setup, available layers, and dependencies.
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numpy-ML is a GitHub-hosted library that implements various machine learning algorithms using only NumPy. The project emphasizes rapid experimentation and can be used as a package. It covers a range of ML methods and includes documentation for users. The repository is open source and welcomes contributions.
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Perfect TensorFlow is an experimental wrapper of the TensorFlow C API that enables machine learning in server-side Swift. It builds with Swift Package Manager and can be used as part of the Perfect project or as an independent module. The framework conforms to TensorFlow v1.8.0 C API functionality.
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Polyaxon is a platform for building, training, and monitoring large-scale deep learning applications. It deploys into any data center or cloud provider and supports major deep learning frameworks such as Tensorflow, MXNet, Caffe, and Torch. It manages workloads with container and node management and offers hyperparameter tuning through experiment groups defining a search algorithm and search space.
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A tutorial project for applying machine learning in Python with scikit-learn, a Python module built on numpy and scipy. It aims to teach practical application of scikit-learn algorithms; the stated audience is experienced Python developers familiar with numpy and scipy. Prebuilt PDF and HTML versions, tutorial source and exercise solutions are available, and the material is under a Creative Commons Attribution 3.0 license.
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TensorLayer is an open-source deep learning and reinforcement learning library designed for scientists and engineers. Hosted on GitHub, the project offers design feature documentation, multilingual resources, and extensive example materials to support use.
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Apache MXNet is a deep learning framework mixing symbolic and imperative programming. It includes a dynamic dependency scheduler that parallelizes symbolic and imperative operations, with a graph optimization layer for symbolic execution. The repository was archived on Nov 17, 2023, and is read-only.
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automl-gs is a Python 3 AutoML module that takes an input CSV and a target field and generates a trained machine learning or deep learning model plus native Python code pipelines. It provides a zero code/model definition interface and performs random hyperparameter searches, reporting metrics for each trial in a CSV.
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Awesome AutoDL is a curated list of automated deep learning resources and an in-depth analysis, subtitled that neural architecture search is not the end. It collects papers, libraries and benchmarks covering neural architecture search and hyper-parameter optimization.