Search Results for "source-han" - Page 11
Sort By:
Showing 1158 open source projects for "source-han"
View related business solutions-
Streamline Azure Security with Palo Alto Networks VM-SeriesImprove your security posture and reduce incident response time. Use the VM-Series to natively analyze Azure traffic and dynamically drive policy updates based on workload changes.
-
Auth0 B2B Essentials: SSO, MFA, and RBAC Built InAuth0's B2B Essentials plan gives you everything you need to ship secure multi-tenant apps. Unlimited orgs, enterprise SSO, RBAC, audit log streaming, and higher auth and API limits included. Add on M2M tokens, enterprise MFA, or additional SSO connections as you scale.
-
1
Opacus
Training PyTorch models with differential privacy
...Vectorized per-sample gradient computation that is 10x faster than micro batching. Supports most types of PyTorch models and can be used with minimal modification to the original neural network. Open source, modular API for differential privacy research. Everyone is welcome to contribute. ML practitioners will find this to be a gentle introduction to training a model with differential privacy as it requires minimal code changes. Differential Privacy researchers will find this easy to experiment and tinker with, allowing them to focus on what matters. -
2
Denoising Diffusion Probabilistic Model
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model. -
3
TorchIO
Medical imaging toolkit for deep learning
TorchIO is an open-source Python library for efficient loading, preprocessing, augmentation and patch-based sampling of 3D medical images in deep learning, following the design of PyTorch. It includes multiple intensity and spatial transforms for data augmentation and preprocessing. These transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity (bias) or k-space motion artifacts. ... -
4
Metarank
A low code Machine Learning service that personalizes articles
Metarank is a service that can personalize any type of content: product listings, articles, recommendations and search results in 3 easy steps with a few lines of code. It’s often considered "too risky" to spend 6+ months on an in-house moonshot project to reinvent the wheel without an experienced team and no existing open-source tools. Metarank makes it easy not only for Amazon to do personalization but for everyone else. Ingest historical item listings, clicks and item metadata so Metarank can find hidden dependencies in the data using our simple JSON format.No Machine Learning experience is required, run our CLI tool with a set of features in a YAML configuration. ... -
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.
-
5
GROBID
A machine learning software for extracting information
GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents such as PDF into structured XML/TEI encoded documents with a particular focus on technical and scientific publications. First developments started in 2008 as a hobby. In 2011 the tool has been made available in open source. Work on GROBID has been steady as a side project since the beginning and is expected to continue as such. Header extraction and parsing from article in PDF format. The extraction here covers the usual bibliographical information (e.g. title, abstract, authors, affiliations, keywords, etc.). References extraction and parsing from articles in PDF format, around .87 F1-score against on an independent PubMed Central set of 1943 PDF containing 90,125 references, and around .89 on a similar bioRxiv set of 2000 PDF (using the Deep Learning citation model). ... -
6
Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides native Java development experience and functions like any other regular Java library. You don't have to be a machine learning/deep learning expert to get started. You can use your existing Java expertise as an on-ramp to learn and use machine learning and deep learning. -
7
BindsNET
Simulation of spiking neural networks (SNNs) using PyTorch
A Python package used for simulating spiking neural networks (SNNs) on CPUs or GPUs using PyTorch Tensor functionality. BindsNET is a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning. This package is used as part of ongoing research on applying SNNs to machine learning (ML) and reinforcement learning (RL) problems in the Biologically Inspired Neural & Dynamical Systems (BINDS) lab. -
8
Adversarial Robustness Toolbox
Adversarial Robustness Toolbox (ART) - Python Library for ML security
Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to evaluate, defend, certify and verify Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, sci-kit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio,... -
9
Intel Extension for PyTorch
A Python package for extending the official PyTorch
Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) Vector Neural Network Instructions (VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel... -
Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
10
fastdup
An unsupervised and free tool for image and video dataset analysis
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale. -
11
Physical Symbolic Optimization (Φ-SO)
Physical Symbolic Optimization
Physical Symbolic Optimization (Φ-SO) - A symbolic optimization package built for physics. Symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms. -
12
Llama Recipes
Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT method
The 'llama-recipes' repository is a companion to the Meta Llama models. We support the latest version, Llama 3.1, in this repository. The goal is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here showcase how to run... -
13
NVIDIA FLARE
NVIDIA Federated Learning Application Runtime Environment
NVIDIA Federated Learning Application Runtime Environment NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration. NVIDIA FLARE is built on a componentized architecture that allows you to take federated learning workloads from research and simulation to real-world production deployment. -
14
TorchMetrics
Machine learning metrics for distributed, scalable PyTorch application
TorchMetrics is a collection of 80+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. The module-based metrics contain internal metric states (similar to the parameters of the PyTorch module) that automate accumulation and synchronization across devices! Automatic accumulation over multiple batches. Automatic... -
15
NannyML
Detecting silent model failure. NannyML estimates performance
NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. -
16
Gen.jl
A general-purpose probabilistic programming system
An open-source stack for generative modeling and probabilistic inference. Gen’s inference library gives users building blocks for writing efficient probabilistic inference algorithms that are tailored to their models, while automating the tricky math and the low-level implementation details. Gen helps users write hybrid algorithms that combine neural networks, variational inference, sequential Monte Carlo samplers, and Markov chain Monte Carlo. -
17
SAHI
A lightweight vision library for performing large object detection
...Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection. -
18
The SpeechBrain Toolkit
A PyTorch-based Speech Toolkit
SpeechBrain is an open-source and all-in-one conversational AI toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. SpeechBrain supports state-of-the-art methods for end-to-end speech recognition, including models based on CTC, CTC+attention, transducers, transformers, and neural language models relying on recurrent neural networks and transformers. -
19
oneDNN
oneAPI Deep Neural Network Library (oneDNN)
This software was previously known as Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) and Deep Neural Network Library (DNNL). oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. oneDNN is part of oneAPI. The library is optimized for Intel(R) Architecture Processors, Intel Processor Graphics and Xe Architecture graphics. oneDNN has experimental support for the following architectures: Arm* 64-bit Architecture (AArch64), NVIDIA* GPU, OpenPOWER* Power ISA (PPC64), IBMz* (s390x), and RISC-V. oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs. ... -
20
Finance
150+ quantitative finance Python programs
Finance is a repository that compiles structured notes and educational material related to financial analysis, markets, and quantitative finance concepts. The project focuses on explaining key principles used in finance and investment analysis, including topics such as financial statements, valuation models, portfolio theory, and financial markets. The repository is designed as a study reference for students and professionals who want to understand financial systems and the analytical... -
21
mosaicml composer
Supercharge Your Model Training
composer is a deep learning training framework built on PyTorch and designed to make large-scale model training more efficient, scalable, and customizable. At the center of the project is a highly optimized Trainer abstraction that simplifies the management of training loops, parallelization, metrics, logging, and data loading. The framework is intended for modern workloads that may span anything from a single GPU to very large distributed training environments, which makes it suitable for... -
22
Model Zoo
Please do not feed the models
FluxML Model Zoo is a collection of demonstration models built with the Flux machine learning library in Julia. The repository provides ready-to-run implementations across multiple domains, including computer vision, natural language processing, and reinforcement learning. Each model is organized into its own project folder with pinned package versions, ensuring reproducibility and stability. The examples serve both as educational tools for learning Flux and as practical starting points for... -
23
Katib
Automated Machine Learning on Kubernetes
Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports Hyperparameter Tuning, Early Stopping and Neural Architecture Search. Katib is a project that is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML frameworks, such as TensorFlow, Apache MXNet, PyTorch, XGBoost, and others. Katib can perform training jobs using any Kubernetes Custom... -
24
Optax
Optax is a gradient processing and optimization library for JAX
Optax is a gradient processing and optimization library for JAX. It is designed to facilitate research by providing building blocks that can be recombined in custom ways in order to optimize parametric models such as, but not limited to, deep neural networks. We favor focusing on small composable building blocks that can be effectively combined into custom solutions. Others may build upon these basic components in more complicated abstractions. Whenever reasonable, implementations prioritize... -
25
Hamilton DAGWorks
Helps scientists define testable, modular, self-documenting dataflow
Hamilton is a lightweight Python library for directed acyclic graphs (DAGs) of data transformations. Your DAG is portable; it runs anywhere Python runs, whether it's a script, notebook, Airflow pipeline, FastAPI server, etc. Your DAG is expressive; Hamilton has extensive features to define and modify the execution of a DAG (e.g., data validation, experiment tracking, remote execution). To create a DAG, write regular Python functions that specify their dependencies with their parameters. As...