Search Results for "machine" - Page 4
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Showing 1387 open source projects for "machine"
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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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Professional Services
Common solutions and tools developed by Google Cloud
Professional Services repository is a collection of real-world solutions, tools, and reference implementations developed by Google Cloud’s Professional Services team to address common enterprise challenges. Unlike simple sample repositories, it focuses on production-oriented use cases such as data pipelines, machine learning workflows, infrastructure automation, and security management. The repository contains a wide variety of projects, including tools for validating data migrations, generating large datasets for testing, building analytics dashboards, and automating policy enforcement in cloud environments. These solutions are intended to serve as blueprints that organizations can adapt and extend for their own needs rather than turnkey products. ... -
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DeepXDE
A library for scientific machine learning & physics-informed learning
DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms. Physics-informed neural network (PINN). Solving different problems. Solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [SIAM Rev.] Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. -
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lightning AI
The most intuitive, flexible, way for researchers to build models
...Find templates (Lightning Apps), modify them and publish your own. Lightning Apps can even be full standalone ML products! Run on your laptop for free! Download the code and type 'lightning run app'. Feel free to ssh into any machine and run from there as well. In research, we often have multiple separate scripts to train models, finetune them, collect results and more. -
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MLJBase.jl
Core functionality for the MLJ machine learning framework
Repository for developers that provides core functionality for the MLJ machine learning framework. MLJ is a Julia framework for combining and tuning machine learning models. This repository provides core functionality for MLJ. -
PRTG Catches Network Issues Before They Cause DowntimeReactive troubleshooting usually means hearing about a problem from frustrated users, not your monitoring tool. PRTG sets threshold-based alerts across devices, servers and applications, notifying your team by email, SMS or push the moment a metric crosses a set limit. That means catching a failing disk or overloaded server before it becomes an outage and getting time back from firefighting. Start a free trial and set your first alerts today.
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diff-so-fancy
Make your diffs human readable instead of machine readable
diff-so-fancy strives to make your diffs human readable instead of machine readable. This helps improve code quality and helps you spot defects faster. diff-so-fancy is also available from NPM, Nix, brew, and as a package on Arch and Debian Linux. Windows users may need to install MinGW or the Windows subsystem for Linux. By default, the separator for the file header uses Unicode line-drawing characters. -
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Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used across DeepMind. ... -
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IVY
The Unified Machine Learning Framework
Take any code that you'd like to include. For example, an existing TensorFlow model, and some useful functions from both PyTorch and NumPy libraries. Choose any framework for writing your higher-level pipeline, including data loading, distributed training, analytics, logging, visualization etc. Choose any backend framework which should be used under the hood, for running this entire pipeline. Choose the most appropriate device or combination of devices for your needs. DeepMind releases an... -
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DeepCTR
Package of deep-learning based CTR models
DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based... -
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Zydis
Fast and lightweight x86/x86-64 disassembler and code generation lib
...Its efficiency makes it particularly useful in scenarios where large volumes of code need to be analyzed quickly. Overall, zydis serves as a reliable and high-performance tool for understanding and working with machine-level instructions. -
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Google Translate PHP
Free Google Translate API PHP Package
A simple and effective PHP library for translating text using Google Translate without needing an API key. It allows developers to integrate real-time translation features into their applications with minimal setup and supports multiple languages, leveraging Google Translate’s unofficial endpoint. -
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Netis Cloud Probe
A Software Probe for network packet capturing and forwarding in Cloud
Netis Cloud Probe (Packet Agent, name used before)is an open source project to deal with such a situation: it captures packets on Machine A but has to use them on Machine B. This case is very common when you try to monitor network traffic in the LAN. -
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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
Beyond its much publicized success in attaining superhuman level at games such as Chess and Go, DeepMind's AlphaZero algorithm illustrates a more general methodology of combining learning and search to explore large combinatorial spaces effectively. We believe that this methodology can have exciting applications in many different research areas. Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++)... -
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C/C++ for Visual Studio Code
Repository for the Microsoft C/C++ extension for VS Code
The C/C++ extension adds language support for C/C++ to Visual Studio Code, including features such as IntelliSense and debugging. C/C++ support for Visual Studio Code is provided by a Microsoft C/C++ extension to enable cross-platform C and C++ development on Windows, Linux, and macOS. C++ is a compiled language meaning your program's source code must be translated (compiled) before it can be run on your computer. VS Code is first and foremost an editor, and relies on command-line tools to... -
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Byte Buddy
Runtime code generation for the Java virtual machine
Byte Buddy is a code generation and manipulation library for creating and modifying Java classes during the runtime of a Java application and without the help of a compiler. Other than the code generation utilities that ship with the Java Class Library, Byte Buddy allows the creation of arbitrary classes and is not limited to implementing interfaces for the creation of runtime proxies. Furthermore, Byte Buddy offers a convenient API for changing classes either manually, using a Java agent or... -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
Welcome to Amazon SageMaker. This projects highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker. If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. -
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Awesome Fraud Detection Research Papers
A curated list of data mining papers about fraud detection
A curated list of data mining papers about fraud detection from several conferences. -
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Otter-Grader
A Python and R autograding solution
...Otter supports local grading through parallel Docker containers, grading using the autograder platforms of 3rd party learning management systems (LMSs), the deployment of an Otter-managed grading virtual machine, and a client package that allows students to run public checks on their own machines. Otter is designed to grade Python scripts and Jupyter Notebooks, and is compatible with a few different LMSs, including Canvas and Gradescope. -
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Multipass
Multipass orchestrates virtual Ubuntu instances
Multipass is a lightweight VM manager for Linux, Windows and macOS. It's designed for developers who want a fresh Ubuntu environment with a single command. It uses KVM on Linux, Hyper-V on Windows and QEMU on macOS to run the VM with minimal overhead. It can also use VirtualBox on Windows and macOS. Multipass will fetch images for you and keep them up to date. Since it supports metadata for cloud-init, you can simulate a small cloud deployment on your laptop or workstation. -
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Karpathy-Inspired Claude Code Guidelines
A single CLAUDE.md file to improve Claude Code behavior
...It covers topics like implementing backpropagation from scratch, understanding convolutional and recurrent networks, building simple training loops, and exploring real datasets with hands-on code. This collection makes abstract theoretical ideas concrete by walking learners through real code and tangible outcomes, helping demystify parts of machine learning that often feel opaque in purely textbook settings. -
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Numba
NumPy aware dynamic Python compiler using LLVM
Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN. You don't need to replace the Python interpreter, run a separate compilation step, or even have a C/C++ compiler installed. -
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Elixir
Dynamic, functional language designed for building scalable apps
...All Elixir code runs inside lightweight threads of execution (called processes) that are isolated and exchange information via messages. Due to their lightweight nature, it is not uncommon to have hundreds of thousands of processes running concurrently in the same machine. Isolation allows processes to be garbage collected independently, reducing system-wide pauses, and using all machine resources as efficiently as possible (vertical scaling). Processes are also able to communicate with other processes running on different machines in the same network. -
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Metaflow
A framework for real-life data science
Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support,... -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers,... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of...
