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@@ -24,7 +24,7 @@ Further resources:
### Frameworks and Libraries
<!-- MarkdownTOC depth=4 -->
<!-- Contents-->
- [Awesome Machine Learning ![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](#awesome-machine-learning-)
- [Table of Contents](#table-of-contents)
- [Frameworks and Libraries](#frameworks-and-libraries)
@@ -41,6 +41,7 @@ Further resources:
- [Speech Recognition](#cpp-speech-recognition)
- [Sequence Analysis](#cpp-sequence-analysis)
- [Gesture Detection](#cpp-gesture-detection)
- [Reinforcement Learning](#cpp-reinforcement-learning)
- [Common Lisp](#common-lisp)
- [General-Purpose Machine Learning](#common-lisp-general-purpose-machine-learning)
- [Clojure](#clojure)
@@ -130,6 +131,7 @@ Further resources:
- [Federated Learning](#python-federated-learning)
- [Kaggle Competition Source Code](#python-kaggle-competition-source-code)
- [Reinforcement Learning](#python-reinforcement-learning)
- [Speech Recognition](#python-speech-recognition)
- [Ruby](#ruby)
- [Natural Language Processing](#ruby-natural-language-processing)
- [General-Purpose Machine Learning](#ruby-general-purpose-machine-learning)
@@ -220,6 +222,7 @@ Further resources:
* [DSSTNE](https://github.com/amznlabs/amazon-dsstne) - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility.
* [DyNet](https://github.com/clab/dynet) - A dynamic neural network library working well with networks that have dynamic structures that change for every training instance. Written in C++ with bindings in Python.
* [Fido](https://github.com/FidoProject/Fido) - A highly-modular C++ machine learning library for embedded electronics and robotics.
* [FlexML](https://github.com/ozguraslank/flexml) - Easy-to-use and flexible AutoML library for Python.
* [igraph](http://igraph.org/) - General purpose graph library.
* [Intel® oneAPI Data Analytics Library](https://github.com/oneapi-src/oneDAL) - A high performance software library developed by Intel and optimized for Intel's architectures. Library provides algorithmic building blocks for all stages of data analytics and allows to process data in batch, online and distributed modes.
* [LightGBM](https://github.com/Microsoft/LightGBM) - Microsoft's fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
@@ -229,6 +232,7 @@ Further resources:
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
* [N2D2](https://github.com/CEA-LIST/N2D2) - CEA-List's CAD framework for designing and simulating Deep Neural Network, and building full DNN-based applications on embedded platforms
* [oneDNN](https://github.com/oneapi-src/oneDNN) - An open-source cross-platform performance library for deep learning applications.
* [Opik](https://www.comet.com/site/products/opik/) - Open source engineering platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. ([Source Code](https://github.com/comet-ml/opik/))
* [ParaMonte](https://github.com/cdslaborg/paramonte) - A general-purpose library with C/C++ interface for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found [here](https://www.cdslab.org/paramonte/).
* [proNet-core](https://github.com/cnclabs/proNet-core) - A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
* [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python that automates machine learning workflows.
@@ -281,6 +285,10 @@ Further resources:
#### Gesture Detection
* [grt](https://github.com/nickgillian/grt) - The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.
<a name="cpp-reinforcement-learning"></a>
#### Reinforcement Learning
* [RLtools](https://github.com/rl-tools/rl-tools) - The fastest deep reinforcement learning library for continuous control, implemented header-only in pure, dependency-free C++ (Python bindings available as well).
<a name="common-lisp"></a>
## Common Lisp
@@ -625,6 +633,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [Auto ML](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration- just give it a .csv file! **[Deprecated]**
* [Convnet.js](https://cs.stanford.edu/people/karpathy/convnetjs/) - ConvNetJS is a JavaScript library for training Deep Learning models[DEEP LEARNING] **[Deprecated]**
* [Creatify MCP](https://github.com/TSavo/creatify-mcp) - Model Context Protocol server that exposes Creatify AI's video generation capabilities to AI assistants, enabling natural language video creation workflows.
* [Clusterfck](https://harthur.github.io/clusterfck/) - Agglomerative hierarchical clustering implemented in JavaScript for Node.js and the browser. **[Deprecated]**
* [Clustering.js](https://github.com/emilbayes/clustering.js) - Clustering algorithms implemented in JavaScript for Node.js and the browser. **[Deprecated]**
* [Decision Trees](https://github.com/serendipious/nodejs-decision-tree-id3) - NodeJS Implementation of Decision Tree using ID3 Algorithm. **[Deprecated]**
@@ -657,6 +666,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [Netron](https://github.com/lutzroeder/netron) - Visualizer for machine learning models.
* [tensor-js](https://github.com/Hoff97/tensorjs) - A deep learning library for the browser, accelerated by WebGL and WebAssembly.
* [WebDNN](https://github.com/mil-tokyo/webdnn) - Fast Deep Neural Network JavaScript Framework. WebDNN uses next generation JavaScript API, WebGPU for GPU execution, and WebAssembly for CPU execution.
* [WebNN](https://webnn.dev) - A new web standard that allows web apps and frameworks to accelerate deep neural networks with on-device hardware such as GPUs, CPUs, or purpose-built AI accelerators.
<a name="javascript-misc"></a>
#### Misc
@@ -719,6 +729,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [Knet](https://github.com/denizyuret/Knet.jl) - Koç University Deep Learning Framework.
* [Flux](https://fluxml.ai/) - Relax! Flux is the ML library that doesn't make you tensor
* [MLJ](https://github.com/alan-turing-institute/MLJ.jl) - A Julia machine learning framework.
* [CluGen](https://github.com/clugen/CluGen.jl/) - Multidimensional cluster generation in Julia.
<a name="julia-natural-language-processing"></a>
#### Natural Language Processing
@@ -873,7 +884,7 @@ on MNIST digits[DEEP LEARNING].
* [Optunity](https://optunity.readthedocs.io/en/latest/) - A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search. Optunity is written in Python but interfaces seamlessly with MATLAB.
* [MXNet](https://github.com/apache/incubator-mxnet/) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
* [Machine Learning in MatLab/Octave](https://github.com/trekhleb/machine-learning-octave) - Examples of popular machine learning algorithms (neural networks, linear/logistic regressions, K-Means, etc.) with code examples and mathematics behind them being explained.
* [MOCluGen](https://github.com/clugen/MOCluGen/) - Multidimensional cluster generation in MATLAB/Octave.
<a name="matlab-data-analysis--data-visualization"></a>
#### Data Analysis / Data Visualization
@@ -1016,6 +1027,7 @@ be
<a name="python-computer-vision"></a>
#### Computer Vision
* [LightlyTrain](https://github.com/lightly-ai/lightly-train) - Pretrain computer vision models on unlabeled data for industrial applications
* [Scikit-Image](https://github.com/scikit-image/scikit-image) - A collection of algorithms for image processing in Python.
* [Scikit-Opt](https://github.com/guofei9987/scikit-opt) - Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm, Artificial Fish Swarm Algorithm in Python)
* [SimpleCV](http://simplecv.org/) - An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux.
@@ -1088,7 +1100,7 @@ be
* [stanford-corenlp-python](https://github.com/dasmith/stanford-corenlp-python) - Python wrapper for [Stanford CoreNLP](https://github.com/stanfordnlp/CoreNLP) **[Deprecated]**
* [CLTK](https://github.com/cltk/cltk) - The Classical Language Toolkit.
* [Rasa](https://github.com/RasaHQ/rasa) - A "machine learning framework to automate text-and voice-based conversations."
* [yase](https://github.com/PPACI/yase) - Transcode sentence (or other sequence) to list of word vector .
* [yase](https://github.com/PPACI/yase) - Transcode sentence (or other sequence) to list of word vector.
* [Polyglot](https://github.com/aboSamoor/polyglot) - Multilingual text (NLP) processing toolkit.
* [DrQA](https://github.com/facebookresearch/DrQA) - Reading Wikipedia to answer open-domain questions.
* [Dedupe](https://github.com/dedupeio/dedupe) - A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
@@ -1101,6 +1113,7 @@ be
* [Haystack](https://github.com/deepset-ai/haystack) - A framework for building industrial-strength applications with Transformer models and LLMs.
* [CometLLM](https://github.com/comet-ml/comet-llm) - Track, log, visualize and evaluate your LLM prompts and prompt chains.
* [Transformers](https://github.com/huggingface/transformers) - A deep learning library containing thousands of pre-trained models on different tasks. The goto place for anything related to Large Language Models.
* [TextCL](https://github.com/alinapetukhova/textcl) - Text preprocessing package for use in NLP tasks.
<a name="python-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning
@@ -1130,7 +1143,7 @@ be
* [einops](https://github.com/arogozhnikov/einops) - Deep learning operations reinvented (for pytorch, tensorflow, jax and others).
* [machine learning](https://github.com/jeff1evesque/machine-learning) - automated build consisting of a [web-interface](https://github.com/jeff1evesque/machine-learning#web-interface), and set of [programmatic-interface](https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.
* [XGBoost](https://github.com/dmlc/xgboost) - Python bindings for eXtreme Gradient Boosting (Tree) Library.
* [ChefBoost](https://github.com/serengil/chefboost) - a lightweight decision tree framework for Python with categorical feature support covering regular decision tree algorithms such as ID3, C4.5, CART, CHAID and regression tree; also some advanved bagging and boosting techniques such as gradient boosting, random forest and adaboost.
* [ChefBoost](https://github.com/serengil/chefboost) - a lightweight decision tree framework for Python with categorical feature support covering regular decision tree algorithms such as ID3, C4.5, CART, CHAID and regression tree; also some advanced bagging and boosting techniques such as gradient boosting, random forest and adaboost.
* [Apache SINGA](https://singa.apache.org) - An Apache Incubating project for developing an open source machine learning library.
* [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python.
* [Featureforge](https://github.com/machinalis/featureforge) A set of tools for creating and testing machine learning features, with a scikit-learn compatible API.
@@ -1153,6 +1166,8 @@ be
* [hebel](https://github.com/hannes-brt/hebel) - GPU-Accelerated Deep Learning Library in Python. **[Deprecated]**
* [Chainer](https://github.com/chainer/chainer) - Flexible neural network framework.
* [prophet](https://facebook.github.io/prophet/) - Fast and automated time series forecasting framework by Facebook.
* [skforecast](https://github.com/skforecast/skforecast) - Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.
* [Feature-engine](https://github.com/feature-engine/feature_engine) - Open source library with an exhaustive battery of feature engineering and selection methods based on pandas and scikit-learn.
* [gensim](https://github.com/RaRe-Technologies/gensim) - Topic Modelling for Humans.
* [tweetopic](https://centre-for-humanities-computing.github.io/tweetopic/) - Blazing fast short-text-topic-modelling for Python.
* [topicwizard](https://github.com/x-tabdeveloping/topic-wizard) - Interactive topic model visualization/interpretation framework.
@@ -1260,7 +1275,7 @@ be
* [Shapash](https://github.com/MAIF/shapash) : Shapash is a Python library that provides several types of visualization that display explicit labels that everyone can understand.
* [Eurybia](https://github.com/MAIF/eurybia): Eurybia monitors data and model drift over time and securizes model deployment with data validation.
* [Colossal-AI](https://github.com/hpcaitech/ColossalAI): An open-source deep learning system for large-scale model training and inference with high efficiency and low cost.
* [dirty_cat](https://github.com/dirty-cat/dirty_cat) - facilitates machine-learning on dirty, non-curated categories. It provides transformers and encoders robust to morphological variants, such as typos.
* [skrub](https://github.com/skrub-data/skrub) - Skrub is a Python library that eases preprocessing and feature engineering for machine learning on dataframes.
* [Upgini](https://github.com/upgini/upgini): Free automated data & feature enrichment library for machine learning - automatically searches through thousands of ready-to-use features from public and community shared data sources and enriches your training dataset with only the accuracy improving features.
* [AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics](https://github.com/Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics): A tutorial to help machine learning researchers to automatically obtain optimized machine learning models with the optimal learning performance on any specific task.
* [SKBEL](https://github.com/robinthibaut/skbel): A Python library for Bayesian Evidential Learning (BEL) in order to estimate the uncertainty of a prediction.
@@ -1270,7 +1285,9 @@ be
* [PyBroker](https://github.com/edtechre/pybroker) - Algorithmic Trading with Machine Learning.
* [Frouros](https://github.com/IFCA/frouros): Frouros is an open source Python library for drift detection in machine learning systems.
* [CometML](https://github.com/comet-ml/comet-examples): The best-in-class MLOps platform with experiment tracking, model production monitoring, a model registry, and data lineage from training straight through to production.
* [Okrolearn](https://github.com/Okerew/okrolearn): A python machine learning library created to combine powefull data analasys features with tensors and machine learning components, while maintaining support for other libraries.
* [Opik](https://github.com/comet-ml/opik): Evaluate, trace, test, and ship LLM applications across your dev and production lifecycles.
* [pyclugen](https://github.com/clugen/pyclugen) - Multidimensional cluster generation in Python.
<a name="python-data-analysis--data-visualization"></a>
#### Data Analysis / Data Visualization
@@ -1333,6 +1350,7 @@ be
* [Lambdo](https://github.com/asavinov/lambdo) - A workflow engine for solving machine learning problems by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation via user-defined (Python) functions.
* [TensorWatch](https://github.com/microsoft/tensorwatch) - Debugging and visualization tool for machine learning and data science. It extensively leverages Jupyter Notebook to show real-time visualizations of data in running processes such as machine learning training.
* [dowel](https://github.com/rlworkgroup/dowel) - A little logger for machine learning research. Output any object to the terminal, CSV, TensorBoard, text logs on disk, and more with just one call to `logger.log()`.
* [Flama](https://github.com/vortico/flama) - Ignite your models into blazing-fast machine learning APIs with a modern framework.
<a name="python-misc-scripts--ipython-notebooks--codebases"></a>
#### Misc Scripts / iPython Notebooks / Codebases
@@ -1467,6 +1485,11 @@ be
* [Maze](https://github.com/enlite-ai/maze) - Application-oriented deep reinforcement learning framework addressing real-world decision problems.
* [RLlib](https://github.com/ray-project/ray) - RLlib is an industry level, highly scalable RL library for tf and torch, based on Ray. It's used by companies like Amazon and Microsoft to solve real-world decision making problems at scale.
* [DI-engine](https://github.com/opendilab/DI-engine) - DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.
* [Gym4ReaL](https://github.com/Daveonwave/gym4ReaL) - Gym4ReaL is a comprehensive suite of realistic environments designed to support the development and evaluation of RL algorithms that can operate in real-world scenarios. The suite includes a diverse set of tasks exposing RL algorithms to a variety of practical challenges.
<a name="python-speech-recognition"></a>
#### Speech Recognition
* [EspNet](https://github.com/espnet/espnet) - ESPnet is an end-to-end speech processing toolkit for tasks like speech recognition, translation, and enhancement, using PyTorch and Kaldi-style data processing.
<a name="ruby"></a>
## Ruby
@@ -1528,6 +1551,7 @@ be
* [RusticSOM](https://github.com/avinashshenoy97/RusticSOM) - A Rust library for Self Organising Maps (SOM).
* [candle](https://github.com/huggingface/candle) - Candle is a minimalist ML framework for Rust with a focus on performance (including GPU support) and ease of use.
* [linfa](https://github.com/rust-ml/linfa) - `linfa` aims to provide a comprehensive toolkit to build Machine Learning applications with Rust
* [delta](https://github.com/delta-rs/delta) - An open source machine learning framework in Rust Δ
#### Deep Learning
@@ -1631,10 +1655,12 @@ be
* [igraph](https://igraph.org/r/) - binding to igraph library - General purpose graph library.
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
* [TDSP-Utilities](https://github.com/Azure/Azure-TDSP-Utilities) - Two data science utilities in R from Microsoft: 1) Interactive Data Exploration, Analysis, and Reporting (IDEAR) ; 2) Automated Modelling and Reporting (AMR).
* [clugenr](https://github.com/clugen/clugenr/) - Multidimensional cluster generation in R.
<a name="r-data-analysis--data-visualization"></a>
#### Data Manipulation | Data Analysis | Data Visualization
* [data.table](https://rdatatable.gitlab.io/data.table/) - `data.table` provides a high-performance version of base R’s `data.frame` with syntax and feature enhancements for ease of use, convenience and programming speed.
* [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) - A data manipulation package that helps to solve the most common data manipulation problems.
* [ggplot2](https://ggplot2.tidyverse.org/) - A data visualization package based on the grammar of graphics.
* [tmap](https://cran.r-project.org/web/packages/tmap/vignettes/tmap-getstarted.html) for visualizing geospatial data with static maps and [leaflet](https://rstudio.github.io/leaflet/) for interactive maps
@@ -1722,7 +1748,8 @@ be
* [SwiftLearner](https://github.com/valdanylchuk/swiftlearner/) - Simply written algorithms to help study ML or write your own implementations.
* [Smile](https://haifengl.github.io/) - Statistical Machine Intelligence and Learning Engine.
* [doddle-model](https://github.com/picnicml/doddle-model) - An in-memory machine learning library built on top of Breeze. It provides immutable objects and exposes its functionality through a scikit-learn-like API.
* [TensorFlow Scala](https://github.com/eaplatanios/tensorflow_scala) - Strongly-typed Scala API for TensorFlow.
* [TensorFlow Scala](https://github.com/eaplatanios/tensorflow_scala) - Strongly-typed Scala API for TensorFlow.
* [isolation-forest](https://github.com/linkedin/isolation-forest) - A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
<a name="scheme"></a>
## Scheme
@@ -1770,10 +1797,12 @@ be
<a name="tools-misc"></a>
#### Misc
* [Wallaroo.AI](https://wallaroo.ai/) - Production AI plaftorm for deploying, managing, and observing any model at scale across any environment from cloud to edge. Let's go from python notebook to inferencing in minutes.
* [Infinity](https://github.com/infiniflow/infinity) - The AI-native database built for LLM applications, providing incredibly fast vector and full-text search. Developed using C++20
* [Synthical](https://synthical.com) - AI-powered collaborative research environment. You can use it to get recommendations of articles based on reading history, simplify papers, find out what articles are trending, search articles by meaning (not just keywords), create and share folders of articles, see lists of articles from specific companies and universities, and add highlights.
* [Humanloop](https://humanloop.com) – Humanloop is a platform for prompt experimentation, finetuning models for better performance, cost optimization, and collecting model generated data and user feedback.
* [Qdrant](https://qdrant.tech) – Qdrant is [open source](https://github.com/qdrant/qdrant) vector similarity search engine with extended filtering support, written in Rust.
* [Localforge](https://localforge.dev/) – Is an [open source](https://github.com/rockbite/localforge) on-prem AI coding autonomous assistant that lives inside your repo, edits and tests files at SSD speed. Think Claude Code but with UI. plug in any LLM (OpenAI, Gemini, Ollama, etc.) and let it work for you.
* [milvus](https://milvus.io) – Milvus is [open source](https://github.com/milvus-io/milvus) vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.
* [Weaviate](https://www.semi.technology/developers/weaviate/current/) – Weaviate is an [open source](https://github.com/semi-technologies/weaviate) vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale.
* [txtai](https://github.com/neuml/txtai) - Build semantic search applications and workflows.
@@ -1787,13 +1816,14 @@ be
* [DVClive](https://github.com/iterative/dvclive) - Python library for experiment metrics logging into simply formatted local files.
* [VDP](https://github.com/instill-ai/vdp) - open source visual data ETL to streamline the end-to-end visual data processing pipeline: extract unstructured visual data from pre-built data sources, transform it into analysable structured insights by Vision AI models imported from various ML platforms, and load the insights into warehouses or applications.
* [Kedro](https://github.com/quantumblacklabs/kedro/) - Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models.
* [Hamilton](https://github.com/dagworks-inc/hamilton) - a lightweight library to define data transformations as a directed-acyclic graph (DAG). It helps author reliable feature engineering and machine learning pipelines, and more.
* [guild.ai](https://guild.ai/) - Tool to log, analyze, compare and "optimize" experiments. It's cross-platform and framework independent, and provided integrated visualizers such as tensorboard.
* [Sacred](https://github.com/IDSIA/sacred) - Python tool to help you configure, organize, log and reproduce experiments. Like a notebook lab in the context of Chemistry/Biology. The community has built multiple add-ons leveraging the proposed standard.
* [Comet](https://www.comet.com/) - ML platform for tracking experiments, hyper-parameters, artifacts and more. It's deeply integrated with over 15+ deep learning frameworks and orchestration tools. Users can also use the platform to monitor their models in production.
* [MLFlow](https://mlflow.org/) - platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework and language agnostic, take a look at all the built-in integrations.
* [Weights & Biases](https://www.wandb.com/) - Machine learning experiment tracking, dataset versioning, hyperparameter search, visualization, and collaboration
* More tools to improve the ML lifecycle: [Catalyst](https://github.com/catalyst-team/catalyst), [PachydermIO](https://www.pachyderm.io/). The following are GitHub-alike and targeting teams [Weights & Biases](https://www.wandb.com/), [Neptune.ai](https://neptune.ai/), [Comet.ml](https://www.comet.ml/), [Valohai.ai](https://valohai.com/), [DAGsHub](https://DAGsHub.com/).
* [Arize AI](https://www.arize.com) - Model validaiton and performance monitoring, drift detection, explainability, visualization across structured and unstructured data
* [Arize AI](https://www.arize.com) - Model validation and performance monitoring, drift detection, explainability, visualization across structured and unstructured data
* [MachineLearningWithTensorFlow2ed](https://www.manning.com/books/machine-learning-with-tensorflow-second-edition) - a book on general purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1.
* [m2cgen](https://github.com/BayesWitnesses/m2cgen) - A tool that allows the conversion of ML models into native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart) with zero dependencies.
* [CML](https://github.com/iterative/cml) - A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull/merge requests. Framework & language agnostic.
@@ -1804,6 +1834,9 @@ be
* [DockerDL](https://github.com/matifali/dockerdl) - Ready to use deeplearning docker images.
* [Aqueduct](https://github.com/aqueducthq/aqueduct) - Aqueduct enables you to easily define, run, and manage AI & ML tasks on any cloud infrastructure.
* [Ambrosia](https://github.com/reactorsh/ambrosia) - Ambrosia helps you clean up your LLM datasets using _other_ LLMs.
* [Fiddler AI](https://www.fiddler.ai) - The all-in-one AI Observability and Security platform for responsible AI. It provides monitoring, analytics, and centralized controls to operationalize ML, GenAI, and LLM applications with trust. Fiddler helps enterprises scale LLM and ML deployments to deliver high performance AI, reduce costs, and be responsible in governance.
* [Maxim AI](https://getmaxim.ai) - The agent simulation, evaluation, and observability platform helping product teams ship their AI applications with the quality and speed needed for real-world use.
* [Agentic Radar](https://github.com/splx-ai/agentic-radar) - Open-source CLI security scanner for agentic workflows. Scans your workflow’s source code, detects vulnerabilities, and generates an interactive visualization along with a detailed security report. Supports LangGraph, CrewAI, n8n, OpenAI Agents, and more.
<a name="books"></a>
## Books
@@ -1812,14 +1845,19 @@ be
* [Grokking Machine Learning](https://www.manning.com/books/grokking-machine-learning) - Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math.
* [Machine Learning Bookcamp](https://www.manning.com/books/machine-learning-bookcamp) - Learn the essentials of machine learning by completing a carefully designed set of real-world projects.
* [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1098125975) - Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
* [Machine Learning Books for Beginners](https://www.appliedaicourse.com/blog/machine-learning-books/) - This blog provides a curated list of introductory books to help aspiring ML professionals to grasp foundational machine learning concepts and techniques.
<a name="credits"></a>
* [Netron](https://netron.app/) - An opensource viewer for neural network, deep learning and machine learning models
* [Teachable Machine](https://teachablemachine.withgoogle.com/) - Train Machine Learning models on the fly to recognize your own images, sounds, & poses.
* [Pollinations.AI](https://pollinations.ai) - Free, no-signup APIs for text, image, and audio generation with no API keys required. Offers OpenAI-compatible interfaces and React hooks for easy integration.
* [Model Zoo](https://modelzoo.co/) - Discover open source deep learning code and pretrained models.
## Credits
* Some of the python libraries were cut-and-pasted from [vinta](https://github.com/vinta/awesome-python)
* References for Go were mostly cut-and-pasted from [gopherdata](https://github.com/gopherdata/resources/tree/master/tooling)
[machinelearning.md Github](https://github.com/josephmisiti/awesome-machine-learning
)