Open Source JavaScript Machine Learning Software
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JavaScript Machine Learning Software
View 446 business solutionsBrowse free open source JavaScript Machine Learning Software and projects below. Use the toggles on the left to filter open source JavaScript Machine Learning Software by OS, license, language, programming language, and project status.
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Machine Learning Systems
Introduction to Machine Learning Systems
Machine Learning Systems is an open educational repository that serves as the source and learning stack for the Machine Learning Systems textbook, a project focused on teaching how to engineer AI systems that work reliably in real-world environments. Rather than concentrating only on model training, the material emphasizes the broader discipline of AI engineering, covering efficiency, reliability, deployment, and evaluation across the full lifecycle of intelligent systems. The repository includes textbook content, supporting labs, and companion tools such as TinyTorch to help learners move from theory to hands-on experimentation. Its mission is to establish AI systems engineering as a foundational discipline alongside traditional software and computer engineering. The project is structured to guide users through reading, building, and deploying workflows, including running labs on edge devices like Arduino and Raspberry Pi. -
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MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
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Netron
Visualizer for neural network, deep learning, machine learning models
Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX, Keras, TensorFlow Lite, Caffe, Darknet, Core ML, MNN, MXNet, ncnn, PaddlePaddle, Caffe2, Barracuda, Tengine, TNN, RKNN, MindSpore Lite, and UFF. Netron has experimental support for TensorFlow, PyTorch, TorchScript, OpenVINO, Torch, Arm NN, BigDL, Chainer, CNTK, Deeplearning4j, MediaPipe, ML.NET, scikit-learn, TensorFlow.js. There is an extense variety of sample model files to download or open using the browser version. It is supported by macOS, Windows, Linux, Python Server and browser. -
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StemRoller
Isolate vocals, drums, bass, and other instrumental stems from songs
StemRoller is the first free app that enables you to separate vocal and instrumental stems from any song with a single click! StemRoller uses Facebook's state-of-the-art Demucs algorithm for demixing songs and integrates search results from YouTube. Simply type the name/artist of any song into the search bar and click the Split button that appears in the results! You'll need to wait several minutes for splitting to complete. Once stems have been extracted, you'll see an Open button next to the song - click that to access your stems! Using StemRoller couldn't be easier - just head to the StemRoller website or the releases page and download the latest version! That bundle includes everything you need to split stems. -
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MagicMirror²
Modular smart mirror platform with a list of installable modules
MagicMirror² is Open Source, free and maintained by a big group of enthusiasts. Got a nice idea? Send us a pull request and become a part of the big list of contributors. The core of MagicMirror² contains a strong API which allows 3rd party developers to build additional modules. Modules you can use. Modules you can develop. Read our extensive documentation to find out everything you want to know about the MagicMirror² project. The full API description allows you to build your own modules. On the forum you will find a big list of MagicMirror² enthusiasts. Share your ideas, ask your questions and get support. The perfect place for you to start. MagicMirror² has an extensively documentated API. It allows you to built your own module backed by a powerful backend. Check out the API documentation for more information and start developing today. -
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TensorFlow
TensorFlow is an open source library for machine learning
Originally developed by Google for internal use, TensorFlow is an open source platform for machine learning. Available across all common operating systems (desktop, server and mobile), TensorFlow provides stable APIs for Python and C as well as APIs that are not guaranteed to be backwards compatible or are 3rd party for a variety of other languages. The platform can be easily deployed on multiple CPUs, GPUs and Google's proprietary chip, the tensor processing unit (TPU). TensorFlow expresses its computations as dataflow graphs, with each node in the graph representing an operation. Nodes take tensors—multidimensional arrays—as input and produce tensors as output. The framework allows for these algorithms to be run in C++ for better performance, while the multiple levels of APIs let the user determine how high or low they wish the level of abstraction to be in the models produced. Tensorflow can also be used for research and production with TensorFlow Extended. -
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Teachable Machine
Explore how machine learning works, live in the browser
Teachable Machine is the open-source implementation of an experimental machine learning tool created by Google Creative Lab that allows users to train simple machine learning models directly in a web browser. The project demonstrates how neural networks can be trained interactively using images captured from a webcam or other inputs without requiring programming knowledge. Users can provide example images for different categories, and the system trains a model that learns to classify those inputs in real time. The project is built using web technologies and the TensorFlow.js ecosystem, enabling machine learning models to run locally within the browser environment. Because the training occurs locally, the system can respond quickly to new examples and provide immediate feedback to users. -
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Universal Data Tool
Collaborate & label any type of data, images, text, or documents etc.
An open-source tool and library for creating and labeling datasets of images, audio, text, documents and video in an open data format. The Universal Data Tool can be used by anyone on your team, no data or programming skills needed. Simplicity without sacrificing any powerful developer features and integrations. Use the Universal Data Tool directly from a web browser or with a Windows, Mac or Linux desktop application. Join a link to a collaborative session and see dataset samples from team members complete in real-time. Import from your S3 buckets easily with IAM or Cognito authentication. Working together, we can accomplish more. The Universal Data Tool was built to bring together the best ideas from different machine learning communities. Upload your dataset to Courses to create a training course. Testing and exercises validate that your workforce knows exactly how the data should be labeled. Get started in less than a minute. Courses uses administrator links. No sign up needed. -
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KoboldAI
Your gateway to GPT writing
This is a browser-based front-end for AI-assisted writing with multiple local & remote AI models. It offers the standard array of tools, including Memory, Author's Note, World Info, Save & Load, adjustable AI settings, formatting options, and the ability to import existing AI Dungeon adventures. You can also turn on Adventure mode and play the game like AI Dungeon Unleashed. Stories can be played like a Novel, a text adventure game or used as a chatbot with an easy toggles to change between the multiple gameplay styles. This makes KoboldAI both a writing assistant, a game and a platform for so much more. The way you play and how good the AI will be depends on the model or service you decide to use. No matter if you want to use the free, fast power of Google Colab, your own high end graphics card, an online service you have an API key for (Like OpenAI or Inferkit) or if you rather just run it slower on your CPU you will be able to find a way to use KoboldAI that works for you. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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AI-Blocks
A powerful and intuitive WYSIWYG to create Machine Learning models
A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models! The concept of AI-Blocs is to have a simple scene with draggable objects that have scripts attached to them. The model can be run directly on the editor or be exported to a standalone script that runs on Tensorflow. Variables are parsed from python scripts and can be edited from the AI-Blocs properties panel. To run your model simply press the "Play" button and let the magic happen! The project requires Python and Tensorflow to run projects. You can still create and edit projects without these dependencies. To run AI-Blocs, download the project archive and launch AI-Blocs.exe. -
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Sklearn TensorFlow
Sklearn and TensorFlow: A Practical Guide to Machine Learning
Sklearn TensorFlow repository is an open-source project that provides a Chinese translation of the widely known book Hands-On Machine Learning with Scikit-Learn and TensorFlow. It aims to make practical machine learning education more accessible to Chinese-speaking learners by translating the technical explanations, examples, and exercises from the original English material. The repository organizes the content as structured documentation that can be compiled into multiple formats such as HTML, PDF, EPUB, and MOBI, allowing users to read the material both online and offline. It focuses on teaching core machine learning concepts using Python while demonstrating practical workflows with popular libraries like Scikit-Learn and TensorFlow. The material covers topics ranging from basic machine learning theory to deep learning techniques and model evaluation, enabling learners to build and experiment with models step by step. -
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fe4ml-zh
Feature Engineering for Machine Learning
fe4ml-zh is an open-source project that provides a Chinese translation and structured documentation of the book Feature Engineering for Machine Learning. The repository aims to make advanced feature engineering concepts accessible to a broader audience by translating the content and organizing it into readable documentation and code examples. Feature engineering is a critical component of machine learning pipelines because it determines how raw data is transformed into features that algorithms can use effectively. The project explains techniques for creating, selecting, and transforming features in ways that improve model accuracy and robustness. It also discusses the role of domain knowledge, data preprocessing, and statistical reasoning in building effective machine learning models. -
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ml5.js
Friendly machine learning for the web
A neighborly approach to creating and exploring artificial intelligence in the browser. ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies. -
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Machine Learning Yearning
Machine Learning Yearning
Artificial intelligence, machine learning and deep learning are transforming numerous industries. Professor Andrew Ng is currently writing a book on how to build machine learning projects. The point of this book is not to teach traditional machine learning algorithms, but to teach you how to make machine learning algorithms work. Some technical courses in AI will give you a tool, and this book will teach you how to use those tools. If you aspire to be a technical leader in AI and want to learn how to set a direction for your team, this book will help. This book is still a sample draft. In order to make the corresponding Chinese content available to you as soon as possible, the translation time is rushed, and some of the content is inevitably oversight. You can enter the warehouse address through the Github icon in the upper right corner, and make certain modification suggestions. -
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Pwnagotchi
Deep Reinforcement learning instrumenting bettercap for WiFi pwning
Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs. Instead of merely playing Super Mario or Atari games like most reinforcement learning based “AI” (yawn), Pwnagotchi tunes its own parameters over time to get better at pwning WiFi things in the real world environments you expose it to. To give hackers an excuse to learn about reinforcement learning and WiFi networking, and have a reason to get out for more walks. -
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Flamingo Project
Workflow Designer, Hive Editor, Pig Editor, File System Browser
Flamingo is a open-source Big Data Platform that combine a Ajax Rich Web Interface + Workflow Engine + Workflow Designer + MapReduce + Hive Editor + Pig Editor. 1. Easy Tool for big data 2. Use comfortable in Hadoop EcoSystem projects 3. Based GPL V3 License Supporting Pig IDE, Hive IDE, HDFS Browser, Scheduler, Hadoop Job Monitoring, Workflow Engine, Workflow Designer, MapReduce. -
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dashAI
dashAI: an interactive platform for training, evaluating and deploying
dashAI is an open-source, No-code workbench for Exploratory Data Analysis and classical ML. Visual data preparation, multi-model experiments, XAI explainability, and a plugin-based extensible catalog. The platform guides users through a complete, traceable workflow — data ingestion → visual exploration → preprocessing → model training → evaluation → explainability — without writing a single line of code. Each step is explicit and reversible, keeping the user in control rather than delegating decisions to an opaque pipeline. -
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Arabic Corpus
Text categorization, arabic language processing, language modeling
The Arabic Corpus {compiled by Dr. Mourad Abbas ( http://sites.google.com/site/mouradabbas9/corpora ) The corpus Khaleej-2004 contains 5690 documents. It is divided to 4 topics (categories). The corpus Watan-2004 contains 20291 documents organized in 6 topics (categories). Researchers who use these two corpora would mention the two main references: (1) For Watan-2004 corpus ---------------------- M. Abbas, K. Smaili, D. Berkani, (2011) Evaluation of Topic Identification Methods on Arabic Corpora,JOURNAL OF DIGITAL INFORMATION MANAGEMENT,vol. 9, N. 5, pp.185-192. 2) For Khaleej-2004 corpus --------------------------------- M. Abbas, K. Smaili (2005) Comparison of Topic Identification Methods for Arabic Language, RANLP05 : Recent Advances in Natural Language Processing ,pp. 14-17, 21-23 september 2005, Borovets, Bulgary. More useful references to check: ------------------------------------------- https://sites.google.com/site/mouradabbas9/corpora -
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GNNPCSAFT Web App
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT Web App is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. - CPU-compatible desktop build The standard desktop release requires AVX2. For older CPUs, the runtime-compatible variant can be found with the suffix "-rtcompat-". -
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AI Deadlines
AI conference deadline countdowns
AI Deadlines is an open-source project that provides a centralized system for tracking important submission deadlines for major artificial intelligence and machine learning conferences. The repository powers a website that displays countdown timers and structured information for top research conferences across subfields such as computer vision, natural language processing, machine learning, and robotics. The project maintains a curated dataset of conferences that includes metadata such as submission deadlines, abstract deadlines, event dates, conference locations, and related information. Researchers and students use the platform to plan their paper submissions and manage academic schedules without manually tracking multiple conference announcements. The repository includes configuration files and data sources that allow contributors to add or update conferences through pull requests, enabling community-driven maintenance. -
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AlgoWiki
Repository which contains links and resources on different topics
AlgoWiki is an open educational repository that aggregates a large collection of curated resources covering many areas of computer science, programming, and algorithmic problem solving. The project functions as a structured knowledge index that links to tutorials, articles, courses, and research materials across numerous technical domains. Topics include algorithms, machine learning, artificial intelligence, programming languages, web development, and software engineering practices. The repository is organized into directories by subject so that users can easily locate relevant learning materials within a specific discipline. Because it collects external resources rather than implementing software itself, the project acts as a reference library for students and developers who want to explore reliable educational content in computer science. Contributors can expand the repository by adding links, creating new topic categories, or updating outdated resources. -
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This project intends to create a bacteria simulator framework, with some realistic bacteria control methods based on chemical signaling, simple sensors, motors and neural networks. The bacteria will evolve in a genetic algorithm environment.
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CML
Continuous Machine Learning | CI/CD for ML
Continuous Machine Learning (CML) is an open-source CLI tool for implementing continuous integration & delivery (CI/CD) with a focus on MLOps. Use it to automate development workflows, including machine provisioning, model training and evaluation, comparing ML experiments across project history, and monitoring changing datasets. CML can help train and evaluate models, and then generate a visual report with results and metrics, automatically on every pull request. -
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CNN Explainer
Learning Convolutional Neural Networks with Interactive Visualization
In machine learning, a classifier assigns a class label to a data point. For example, an image classifier produces a class label (e.g, bird, plane) for what objects exist within an image. A convolutional neural network, or CNN for short, is a type of classifier, which excels at solving this problem! A CNN is a neural network: an algorithm used to recognize patterns in data. Neural Networks in general are composed of a collection of neurons that are organized in layers, each with their own learnable weights and biases. Let’s break down a CNN into its basic building blocks. A tensor can be thought of as an n-dimensional matrix. In the CNN above, tensors will be 3-dimensional with the exception of the output layer. A neuron can be thought of as a function that takes in multiple inputs and yields a single output. The outputs of neurons are represented above as the red → blue activation maps. -
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ConvNetJS
Deep learning in Javascript to train convolutional neural networks
ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and train Convolutional Networks that process images, and experimental Reinforcement Learning modules, based on Deep Q Learning. The library allows you to formulate and solve Neural Networks in Javascript. If you would like to add features to the library, you will have to change the code in src/ and then compile the library into the build/ directory. The compilation script simply concatenates files in src/ and then minifies the result.
