#machine learning

Sonnet, created by DeepMind researchers, provides a flexible programming structure for machine learning advancements using TensorFlow 2. It emphasizes modularity with `snt.Module`, aiding in the development of neural networks adaptable to various learning forms. Sonnet supports both predefined modules and custom-built ones, such as `snt.Linear`, `snt.Conv2D`, and `snt.nets.MLP`. While lacking an integrated training framework, it empowers users to leverage existing solutions or create new ones, supporting distributed learning. Simple installation and illustrative examples on Google Colab make Sonnet accessible for constructing complex machine learning models.

LLaMA-Factory streamlines the fine-tuning of large language models with advanced algorithms and scalable resources. It supports various models such as LLaMA, LLaVA, and Mistral. Offering capabilities like full-tuning, freeze-tuning, and different quantization methods, it enhances training speed and GPU memory usage efficiency. The platform facilitates experiment tracking and offers fast inference through an intuitive API and interface, suitable for developers improving text generation projects.

AutoTrain Advanced offers an intuitive no-code platform for fast training and deployment of advanced machine learning models, making it accessible to users in just a few steps if the data format is correct. It supports both Colab and Hugging Face Spaces execution, with costs only for used resources. Local installations need Python 3.10 and compatible packages within a conda environment for optimal performance. Users can choose between a graphical interface and command line for flexible workflows, backed by extensive documentation for support.

Label Studio is an open-source tool simplifying data labeling for audio, text, images, videos, and more. It provides multi-user collaboration and project management, integrates with machine learning workflows via REST API and SDK, and supports data import from various sources, enhancing data accuracy through pre-labeling and active learning. Deployable locally or on the cloud, it supports Docker, pip, and Anaconda for flexible operations.

TensorFlow.js is an open-source library designed to train and deploy machine learning models using JavaScript. It enables developers to construct models with straightforward APIs in both browser and Node.js environments. The library supports executing existing TensorFlow models, retraining with client-side data, and using optimized backends like WebGL and WASM for improved performance. Its modular framework accommodates various platforms such as React Native and Node.js, providing tools for model visualization and conversion. This library offers an accessible solution for developers looking to implement machine learning in JavaScript.

This Golang-based open-source chatbot leverages machine learning for customizable and privacy-focused solutions, presenting an alternative to services like DialogFlow. It allows developers to train new models easily and integrate seamlessly with mobile devices through its Progressive Web Application format. With support for multiple languages, this chatbot is designed for flexible and user-friendly deployment in various projects.

ONNX is a community-driven, open-source project that provides a standardized model format to support interoperability across various AI frameworks and tools. It addresses the needs of both deep learning and traditional machine learning, facilitating smooth framework transitions and aiding swift research-to-production deployment. By engaging a wide range of frameworks, tools, and hardware, ONNX enhances AI research and development. The platform invites community collaboration to continually refine its offering, fostering ongoing improvements to support the dynamic field of AI innovation efficiently.

Flux presents a straightforward method for machine learning using a pure-Julia framework. Its efficient abstractions take advantage of Julia's native GPU and automatic differentiation features, facilitating the handling of complex tasks. Compatible with Julia 1.9 or newer versions, it offers simple setup and detailed documentation to support experimentation. A valuable tool for researchers and developers in the Julia ecosystem seeking a versatile machine learning framework.

LLaMA.go is a framework for LLaMA model inference in Golang, reducing GPU dependencies and offering cross-platform support. It emphasizes performance and includes features like multi-threading and a standalone server mode. Future updates will enhance architecture support, performance optimizations, and compatibility with additional AI models.

Discover Rust bindings for the PyTorch C++ API using the tch crate, featuring integration with libtorch for tensor operations and neural network training. Supports both static and dynamic linking with Python compatibility, and includes examples for model training, optimization, and leveraging pre-trained weights, while adhering closely to the original PyTorch API.

YOLOv3, an open-source AI by Ultralytics, excels in object detection, segmentation, and classification. It focuses on speed, accuracy, and ease of use, integrating strategies from extensive R&D. New users can explore detailed guides, join a strong community, and leverage enhanced AI platform integrations, benefiting diverse global developers.

Smile offers a robust platform for machine learning, NLP, and data visualization designed for Java and Scala. It includes advanced capabilities in classification, clustering, regression, and more, leveraging cutting-edge algorithms and data structures. The toolkit seamlessly integrates with Maven and supports APIs for Scala, Kotlin, and Clojure. Featuring visualization tools like SmilePlot and Vega-Lite, it aims to provide efficiency and compatibility across various platforms, making it a valuable resource for developers in data science.

DVC provides tools for managing machine learning projects by versioning data and models, building lightweight pipelines, and tracking experiments. It enables cloud data storage with version control via Git repositories, simplifying experiment comparison and sharing. The VS Code Extension facilitates experiment tracking and data management within the IDE, while DVC's diverse installation options ensure compatibility with various platforms.

DeepSpeech is an open-source speech-to-text engine powered by machine learning, inspired by Baidu's Deep Speech research. It employs TensorFlow, providing comprehensive documentation for installation, usage, and model training at deepspeech.readthedocs.io. Access the latest releases, pre-trained models, and contribution guidelines on GitHub. This project is ideal for developers in search of reliable and scalable speech recognition solutions.

Discover a groundbreaking method in machine learning that emphasizes efficient data labeling and management to reduce manual labor. Originating from Stanford, this project has joined forces with tech giants like Google and Intel, contributing to over sixty peer-reviewed publications and supporting real-world applications. With Snorkel Flow, the project advances into a comprehensive AI platform, incorporating cutting-edge techniques in weak supervision, data augmentation, and multitask learning, offering researchers and practitioners a streamlined process for AI development.

Offers an inclusive toolbox for efficient finetuning of large-scale machine learning models, accessible to the community while supporting diverse optimizers, conversation templates such as Llama-3 and Phi-3, and advanced techniques like speculative decoding and LISA for memory-efficient training. Recognized with the Best Demo Paper Award at NAACL 2024, it provides essential tools for chatbot deployment and model evaluation, suited for professionals aiming to enhance and deploy large models effectively in an objective and unbiased manner.

Lance is an advanced data format designed for machine learning workflows, offering significantly faster random access than Parquet. It supports efficient IO operations crucial for large-scale ML training and integrates well with tools like Pandas, DuckDB, and Polars. Lance features vector search capabilities, automated data versioning, and works seamlessly with Apache Arrow, making it suitable for a range of applications such as search engines and robotics. The project is actively developed and open to community contributions for improvements. Discover its streamlined and adaptable structure to accelerate ML development.

This tutorial provides a detailed guide on developing a machine learning pipeline with PyTorch. It involves creating custom datasets, exploring traditional algorithms, and transitioning to deep learning. Based on a Harvard graduate course project, it includes updated PyTorch code and clear setup instructions. Available in both HTML and IPython Notebook formats, it is designed for those aiming to expand their machine learning knowledge.

The fastai library offers high-level and low-level components to support both standard deep learning tasks and innovative model customization. Its architecture takes advantage of Python and PyTorch, ensuring usability and performance. Key features include a GPU-optimized vision library, dynamic callback system, and adaptable data block API. Suitable for various deployment needs, fastai also facilitates integration with existing libraries and efficient model training.

This neutral guide showcases a broad array of materials for understanding and implementing transformer models, applicable from NLP to computer vision. It features overviews, concise technical insights, tutorials, and applicable examples, suitable for learners and professionals interested in transformers. Highlighted elements include detailed illustrations, technical summaries, and important references such as the 'Attention Is All You Need' paper. The guide also offers practical insights into implementation via resources like the HuggingFace Transformers library.

imgaug is a Python library providing a comprehensive range of image augmentation techniques for machine learning. It enables transformation of images through methods such as affine transformations, noise addition, and cropping to improve model durability. The library efficiently handles diverse data types including images, heatmaps, and segmentation maps while optimizing performance. Features like automatic alignment of values and multicore augmentation ease complex operations. Compatible with Anaconda and pip, imgaug's detailed documentation and examples ensure smooth integration.

This guide provides essential resources to learn Large Language Models (LLMs) without needing an advanced background. Stay informed with the latest updates, techniques, and innovations in 2024 while accessing free resources like tutorials, courses, and community forums. Develop skills in areas such as Transformers and NLP through practical exercises and clear explanations. Suitable for all learning styles, the guide enables learners to become proficient in LLMs independently.

AudioGPT is an open-source initiative providing tools for analyzing and creating speech, music, and other audio forms. The project supports tasks such as text-to-speech, style transfer, and speech recognition through models like FastSpeech and whisper. For audio manipulation, it includes tasks like text-to-audio and image-to-audio using models such as Make-An-Audio. It also offers talking head synthesis with GeneFace. As some features are being refined, AudioGPT continuously broadens its functionality for varied audio projects.

Spleeter is a valuable tool for audio source separation that employs pretrained models to achieve swift vocal and instrumental isolation, leveraging TensorFlow for processing speeds up to 100 times faster than real-time on GPU. It can be integrated through command line or as a Python library, supporting two, four, and five-stem separations. Popular among professional audio software developers, Spleeter is easily installed using pip or Docker, catering to developers seeking high-efficiency music demixing that fits smoothly into pre-existing systems.

TensorFlow Serving provides a stable and scalable platform for deploying machine learning models in production environments. It integrates effortlessly with TensorFlow while accommodating different model types and supporting simultaneous operation of multiple model versions. Notable features include gRPC and HTTP inference endpoints, seamless model version updates without client-side code alterations, low latency inference, and efficient GPU batch request handling. This makes it well-suited for environments seeking effective model lifecycle management and version control, enhancing machine learning infrastructures with adaptable and reliable functionalities.

AI Fairness 360 is an open-source toolkit providing tools to detect, explain, and mitigate biases in machine learning models throughout their lifecycle. It includes metrics and algorithms available in Python and R, supporting fields like finance, healthcare, and education. The platform offers interactive experiences, tutorials, and an API for user guidance, and welcomes contributions to expand its capabilities. Detailed documentation ensures ease of use across various systems for effective bias management.

DeepChem provides an open-source toolchain for applying deep learning in areas such as drug discovery, materials science, quantum chemistry, and biology. It offers integration with Python environments and support for TensorFlow, PyTorch, and JAX, enhancing research efficiency. Rich tutorials, easy installation via pip or conda, and active community involvement make DeepChem a valuable resource for molecular machine learning and computational biology. Setting up with Docker and connecting with the community through Discord, DeepChem supports innovation and scientific progress.

AutoGluon automates machine learning tasks to deliver high predictive accuracy with minimal coding effort. It supports diverse data types, including images, text, time series, and tabular data, and is compatible with Python versions 3.8 to 3.11 on Linux, MacOS, and Windows. The tool offers comprehensive documentation, tutorials, and a supportive community through platforms such as Discord and Twitter, making it an effective choice for developers looking to improve machine learning processes efficiently.

Discover a comprehensive course focusing on PyTorch for deep learning, including the latest PyTorch 2.0 tutorial. This hands-on course emphasizes practical coding with sections covering neural network classification, computer vision, transfer learning, custom datasets, and model deployment. Through milestone projects such as FoodVision, gain practical experience and develop a portfolio. This beginner-friendly course uses Google Colab notebooks and video content for understanding deep learning fundamentals.

Designed for those with some experience in machine learning, including familiarity with frameworks like Keras, TensorFlow, or Pytorch, this guide makes Pytorch learning accessible with its optimized, easy-to-follow examples and step-by-step progression. Spend 30 minutes to 2 hours daily over 20 days to effectively incorporate Pytorch into real-world projects. The guide serves as a reliable reference, packed with practical examples, for enhancing application development expertise.

Augmentor is a Python library for machine learning, offering independent image augmentation through a stochastic pipeline. It supports a variety of techniques like rotations and elastic distortions, useful for training neural networks. With multi-threading to boost performance and integration with Keras and PyTorch, it simplifies complex image processing.

Discover a wide array of resources covering machine learning, Golang, PostgreSQL, and distributed systems, organized into detailed chapters for comprehensive understanding. The project embraces collaboration and accuracy by welcoming pull requests for content improvement. By interacting with translated materials, learners can effectively enhance their programming and database skills. This platform integrates educational content with a community feedback loop, forming an essential hub for technology learners and professionals.

Adblock Radio uses machine learning to detect and block ads in real-time radio streams and podcasts. By integrating spectral analysis and audio fingerprinting, it identifies ads, music, and jingles with accuracy. The solution is designed for easy integration, running efficiently on standard laptops with Node.js, with optional Python enhancements. Developers get a straightforward interface for ongoing audio analysis, making it suitable for applications needing ad-filtering capabilities.

The project converts the MXNet code of 'Dive into Deep Learning' to TensorFlow2, specifically addressing the Chinese version. Endorsed by original authorship and incorporating PyTorch adaptation insights, it targets those keen on deep learning via TensorFlow2, demanding only fundamental math and coding skills. Accessible via web and local server through docsify, it invites contributions for ongoing enhancement, recognized as an educational asset by significant AI platforms.

TensorWatch offers flexible debugging and visualization for ML, integrating with Jupyter Notebook, and supporting tools like PyTorch and TensorFlow. Features include custom visuals, lazy logging, and diverse plots, aiding model training and prediction explanations. Requires Python 3.x and Graphviz.

FiftyOne enhances machine learning by enabling efficient dataset visualization and error analysis, improving data quality and model accuracy. This open-source tool supports detailed exploration of data and the evaluation of computer vision models. Users can identify errors and optimize models with greater precision. Participate in its Slack community, read informative articles, and access tutorials to leverage its capabilities. For easy installation, use pip to access its comprehensive features.

PyBroker is a comprehensive Python framework designed for algorithmic trading with a focus on machine learning for optimized strategy performance. It features a high-speed backtesting engine powered by NumPy and Numba, flexible rule and model development, and offers access to historical data from sources such as Alpaca and Yahoo Finance. The framework supports Walkforward Analysis to emulate real-world trading conditions, employs bootstrapping for accurate metrics, and enhances efficiency with data caching and parallelized processing. Suitable for traders in search of data-driven, machine-learning-powered strategies, PyBroker supports Python 3.9+ on all major platforms.

Designed to democratize access to machine learning, Turi Create facilitates the easy development of custom models for individuals without technical expertise. This intuitive tool seamlessly integrates complex tasks such as recommendations, object detection, and image classification into applications, supporting various data types including text, images, audio, and video. With scalable data processing on a single machine, it allows exporting models to Core ML for use within Apple's ecosystem, covering iOS, macOS, watchOS, and tvOS. Turi Create offers flexibility and ease of use, with built-in visualizations for data exploration, and supports a wide range of tasks from regression to style transfer, making it ideal for developers across various domains.

Embark on a journey from experimentation to production with machine learning. Learn to design, develop, and deploy ML applications with industry best practices. Join over 40,000 developers to enhance your skills in MLOps, scaling ML workloads, and creating CI/CD workflows. Suitable for developers, graduates, and leaders, this resource bridges academic knowledge with industry demands, offering a solid foundation in ML system development.

MNN is an advanced deep learning framework designed for efficient and lightweight on-device training and inference. Integrated into over 30 Alibaba applications, it supports various scenarios from live broadcasts to security controls across mobile and IoT devices. Known for its high inference speed, MNN is a key component in the Walle system, recognized at OSDI'22. Its architecture supports a variety of neural network models, leveraging optimized assembly code for enhanced performance. Learn more about MNN's role in device-cloud collaborative machine learning.

Semantic Router enhances AI decision-making through semantic vector spaces, offering faster routing by bypassing traditional LLM processing. Integrations with Cohere and OpenAI support diverse decisions including politics and conversation topics. Provides flexible local or hybrid execution and integrates with Pinecone and Qdrant, boosting AI interaction efficiency.

NN-SVG is a tool for creating neural network architecture diagrams parametrically. It facilitates efficient SVG export and supports various neural network types, using D3 and Three.js libraries. Customizable for color, size, and layout, it aids researchers in saving time and serves as a learning resource.

H2O-3 presents a powerful in-memory platform for distributed, scalable machine learning with user-friendly interfaces in R, Python, Scala, Java, and JSON. It seamlessly integrates with big data technologies such as Hadoop and Spark, offering support for popular algorithms including GLM, XGBoost, Random Forests, and Deep Learning. Its extensible architecture allows developers to integrate custom algorithms and data transformations. Export models for rapid scoring in production environments. Built upon the foundation of H2O-2, it ensures easy installation via PyPI and CRAN for Python and R users, broadening accessibility and usability across various platforms. Comprehensive documentation fosters user engagement and community contribution, while simplifying complex terminology for better understanding.

The project delivers structured and clear tutorials with optimized code for TensorFlow, assisting both novices and seasoned developers in mastering complex deep learning tasks. The repository supplies source code and documentation that clarifies model complexities, fostering an expanding community through tutorials from fundamental operations to sophisticated neural networks, all designed to enhance effective TensorFlow use.

SynapseML makes machine learning accessible across various platforms by simplifying the creation of scalable ML pipelines with Apache Spark. It offers distributed APIs for tasks including text analytics, vision, and anomaly detection. With compatibility across Python, R, Scala, Java, and .NET, it operates efficiently on multi-node clusters. SynapseML supports Spark 3.4+ and Python 3.8+, allowing seamless integration into existing workflows. Its diverse ML capabilities and innovative features, such as Vowpal Wabbit, Cognitive Services, and ONNX on Spark, set it apart from similar tools.

ONNX Runtime optimizes machine learning by accelerating inference and training across platforms. It supports models from frameworks like PyTorch and TensorFlow, and systems like scikit-learn and XGBoost, focusing on hardware optimization. By using multi-node NVIDIA GPUs, it notably reduces training time with minimal changes to PyTorch scripts. With compatibility across various operating systems, ONNX Runtime efficiently enhances performance while cutting costs. Access resources for deeper insights.

AutoKeras, originating from Texas A&M University's DATA Lab, offers a streamlined approach to deep learning with AutoML features. Designed for both beginners and professionals, it provides a user-friendly platform to develop machine learning models with ease. Supporting Python 3.8+ and TensorFlow 2.8+, AutoKeras comes with tutorials and projects to aid learning. Installation through pip enables the application of advanced tools, including image classification. As a community-supported initiative, contributions are encouraged on GitHub. Discover how AutoKeras makes advanced machine learning accessible to all.

Qbot is an AI-based platform for automated quantitative investment. It uses machine learning frameworks such as supervised and reinforcement learning to support the full investment cycle from data collection to live trading. Qbot provides strategy development, backtesting, and simulation tools in a near-real-time setup, with an emphasis on multi-factor models. Some knowledge of Python and trading can be advantageous. Discover how Qbot fills market gaps and resolves trading challenges with its open-source offerings.

MediaPipe provides adaptable machine learning solutions for various platforms, including mobile, web, desktop, edge devices, and IoT. It features comprehensive libraries and resources such as ready-to-use models and cross-platform APIs for easy customization and deployment. With tools like MediaPipe Model Maker and MediaPipe Studio, developers can efficiently tailor and assess solutions. As an open-source initiative, it supports additional customization and community collaboration, facilitating artificial intelligence and machine learning integration into diverse applications.

Metaflow, developed at Netflix, is a user-oriented library that simplifies building and scaling data science projects. It equips scientists with tools for rapid prototyping, experiment tracking, and cloud scalability, offering extensive resources like tutorials and community support for seamless integration.