Search Results for "ml" - Page 6
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Showing 162 open source projects for "ml"
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Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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AI Platform Training and Prediction
Cloud ML Engine repo
...Although the repository has been archived, it still provides extensive reference implementations and practical examples for learning cloud-based ML workflows. -
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related... -
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igel
Machine learning tool that allows you to train and test models
...Besides default values, igel can use auto-ml features to figure out a model that can work great with your data. -
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Music Source Separation
Separate audio recordings into individual sources
Music Source Separation is a PyTorch-based open-source implementation for the task of separating a music (or audio) recording into its constituent sources — for example isolating vocals, instruments, bass, accompaniment, or background from a mixed track. It aims to give users the ability to take any existing song and decompose it into separate stems (vocals, accompaniment, etc.), or to train custom separation models on their own datasets (e.g. for speech enhancement, instrument isolation, or... -
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ML workspace
All-in-one web-based IDE specialized for machine learning
All-in-one web-based development environment for machine learning. The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. ... -
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Photonix Photo Manager
A modern, web-based photo management server
...Run it on your home server and it will let you find the right photo from your collection on any device. Smart filtering is made possible by object recognition, face recognition, location awareness, color analysis and other ML algorithms. This project is currently in development and not feature complete for a version 1.0 yet. If you don't mind putting up with broken parts or want to help out, run the Docker image and give it a go. I'd love for other contributors to get involved. You can move some photos into the folder data/photos and they should get detected and imported immediately. ... -
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Aquila DB
An easy to use Neural Search Engine
Aquila DB is a Neural search engine. In other words, it is a database to index Latent Vectors generated by ML models along with JSON Metadata to perform k-NN retrieval. It is dead simple to set up, language-agnostic, and drop in addition to your Machine Learning Applications. Aquila DB, as of current features is a ready solution for Machine Learning engineers and Data scientists to build Neural Information Retrieval applications out of the box with minimal dependencies. -
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Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying solely on black-box frameworks. This makes the repo suitable for students, hobbyists, or developers who want to deeply understand how ML algorithms work under the hood and experiment with parameter tuning or custom data. Because it's part of the author’s learning-path repositories, it likely is integrated with tutorials, sample datasets, and contextual guidance, which helps users bridge theory. -
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MLOps Course
Learn how to design, develop, deploy and iterate on ML apps
The MLOps Course by Goku Mohandas is an open-source curriculum that teaches how to combine machine learning with solid software engineering to build production-grade ML applications. It is structured around the full lifecycle: data pipelines, modeling, experiment tracking, deployment, testing, monitoring, and iteration. The repository itself contains configuration, code examples, and links to accompanying lessons hosted on the Made With ML site, which provide detailed narrative explanations and diagrams. ... -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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tensorflow_template_application
TensorFlow template application for deep learning
tensorflow_template_application is a template project that demonstrates how to structure scalable applications built with TensorFlow. The repository provides a standardized architecture that helps developers organize machine learning code into clear components such as data processing, model training, evaluation, and deployment. Instead of focusing on a specific algorithm, the project emphasizes software engineering practices that make machine learning systems easier to maintain and extend.... -
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Awesome AI-ML-DL
Awesome Artificial Intelligence, Machine Learning and Deep Learning
...Study notes and a curated list of awesome resources of such topics. This repo is dedicated to engineers, developers, data scientists and all other professions that take interest in AI, ML, DL and related sciences. To make learning interesting and to create a place to easily find all the necessary material. Please contribute, watch, star, fork and share the repo with others in your community. -
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BudgetML
Deploy a ML inference service on a budget in 10 lines of code
Deploy a ML inference service on a budget in less than 10 lines of code. BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end. We built BudgetML because it's hard to find a simple way to get a model in production fast and cheaply. -
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GIMP ML
AI for GNU Image Manipulation Program
...Additionally, operations on images such as edge detection and color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, scikit-image, pillow, pytorch, open-cv, scipy. In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows. -
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Ad-papers
Papers on Computational Advertising
The Ad-papers repository is a curated collection of influential research papers focused on the fields of advertising technology, recommendation systems, and applied machine learning in online platforms. The repository organizes academic and industry papers that explore how machine learning algorithms can be used to improve ad targeting, user modeling, click-through rate prediction, and personalized recommendation systems. These papers represent key developments in large-scale industrial... -
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Libra
Ergonomic machine learning for everyone
An ergonomic machine learning library for non-technical users. Save time. Blaze through ML. -
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Delta ML
Deep learning based natural language and speech processing platform
DELTA is a deep learning-based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users. It helps you to train,... -
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SQLFlow
SQL compiler bridging databases and machine learning workflows
SQLFlow is an open source project designed to bridge the gap between traditional SQL-based data processing and modern machine learning workflows by extending SQL syntax with AI capabilities. It acts as a compiler that translates SQL programs into executable workflows, enabling users to train, evaluate, and deploy machine learning models directly from SQL statements. It integrates with multiple database engines such as MySQL, Hive, and MaxCompute, while also supporting machine learning... -
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Tensor2Tensor
Library of deep learning models and datasets
...However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and compare the results. Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. T2T was developed by researchers and engineers in the Google Brain team and a community of users. It is now deprecated, we keep it running and welcome bug-fixes, but encourage users to use the successor library Trax. -
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TensorNets
High level network definitions with pre-trained weights in TensorFlow
High level network definitions with pre-trained weights in TensorFlow (tested with 2.1.0 >= TF >= 1.4.0). Applicability. Many people already have their own ML workflows and want to put a new model on their workflows. TensorNets can be easily plugged together because it is designed as simple functional interfaces without custom classes. Manageability. Models are written in tf.contrib.layers, which is lightweight like PyTorch and Keras, and allows for ease of accessibility to every weight and end-point. ... -
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FastoCloud PRO
IPTV/NVR/CCTV/Video cloud https://fastocloud.com
IPTV/Video cloud Features: Cross-platform (Linux, MacOSX, FreeBSD, Raspbian/Armbian) GPU/CPU Encode/Decode/Post Processing Stream statistics CCTV Adaptive hls streams Load balancing Temporary urls HLS push EPG scanning Subtitles to text conversions AD insertion Logo overlay Video effects Relays Timeshifts Catchups Playlists Restream/Transcode from online streaming services like Youtube, Twitch Mozaic Many Outputs Physical Inputs Streaming Protocols File Formats Presets Vods/Series server-side support Pay per view channels Channels on demand HTTP Live Streaming (HLS) server-side support Public API, client server communication via JSON RPC Protocol gzip compression Deep learning video analysis Supported deep learning frameworks: Tensorflow NCSDK Caffe ML Hardware: -
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Knock Knock
Get notified when your training ends
Knock Knock is a lightweight Python utility created by the Hugging Face team that allows developers to receive notifications when long-running machine learning tasks finish or fail. Training deep learning models often takes hours or even days, making it inconvenient for engineers to constantly monitor progress manually. The library solves this problem by adding simple decorators or command-line commands that automatically send notifications when a process completes or crashes. These alerts... -
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Machine Learning with TensorFlow
Accompanying source code for Machine Learning with TensorFlow
Machine Learning with TensorFlow is an open repository containing the source code and practical examples that accompany the book Machine Learning with TensorFlow. The project provides numerous code samples demonstrating how to build machine learning models using the TensorFlow framework. These examples illustrate core machine learning concepts such as regression, classification, clustering, and neural networks through practical implementations. The repository includes implementations of... -
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TensorFlow Machine Learning Cookbook
Code for Tensorflow Machine Learning Cookbook
TensorFlow Machine Learning Cookbook repository provides practical code examples and educational materials that accompany the book TensorFlow Machine Learning Cookbook. The repository contains numerous Python scripts and Jupyter notebooks that demonstrate how to implement machine learning algorithms and neural networks using the TensorFlow framework. Each section focuses on a different aspect of machine learning development, including tensor manipulation, model training, optimization... -
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A Machine Learning Course with Python
A course about machine learning with Python
The purpose of this project is to provide a comprehensive and yet simple course in Machine Learning using Python. Machine Learning, as a tool for Artificial Intelligence, is one of the most widely adopted scientific fields. A considerable amount of literature has been published on Machine Learning. The purpose of this project is to provide the most important aspects of Machine Learning by presenting a series of simple and yet comprehensive tutorials using Python. In this project, we built... -
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CrypTen
A framework for Privacy Preserving Machine Learning
...The framework supports both encryption and decryption of tensors and operations such as addition and multiplication over encrypted values. Although not yet production-ready, CrypTen focuses on advancing real-world secure ML applications, such as training and inference over private datasets, without exposing sensitive data.
