Search Results for "pam-python" - Page 4
Sort By:
Showing 525 open source projects for "pam-python"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
RMM Software | Remote Monitoring Platform and ToolsDon’t settle when it comes to managing your clients’ IT infrastructure. Exceed their expectations with ConnectWise RMM, our MSP RMM software that provides proactive tools and NOC services—regardless of device environment. With the number of new vulnerabilities rising each year, smart patching procedures have never been more important. We automatically test and deploy patches when they are viable and restrict patches that are harmful. Get better protection for clients while you spend less time managing endpoints and more time growing your business. It’s tough to locate, afford, and retain quality talent. In fact, 81% of IT leaders say it’s hard to find the recruits they need. Add ConnectWise RMM, NOC services and get the expertise and problem resolution you need to become the advisor your clients demand—without adding headcount.
-
1
MLRun
Machine Learning automation and tracking
MLRun is an open MLOps framework for quickly building and managing continuous ML and generative AI applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous... -
2
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. -
3
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, video, etc.) and machine learning tasks (classification, object detection, generation, certification, etc.). -
4
Lightly
A python library for self-supervised learning on images
...This allows selecting the best core set of samples for model training through advanced filtering. We provide PyTorch, PyTorch Lightning and PyTorch Lightning distributed examples for each of the models to kickstart your project. Lightly requires Python 3.6+ but we recommend using Python 3.7+. We recommend installing Lightly in a Linux or OSX environment. With lightly, you can use the latest self-supervised learning methods in a modular way using the full power of PyTorch. Experiment with different backbones, models, and loss functions. -
Manage queues and reduce operational costsLong queues cost businesses across the world trillions of dollars in lost sales. Customers who experience poor queuing are less likely to stay and recommend your business. Compare the performance of different locations and departments. Monitor the number of visitors waiting, average wait times, and other metrics. Give your staff the tools they need to supercharge your customer service. Recognize your team’s achievements and identify opportunities for growth. Easily measure and share performance results. Use service reports to keep track of KPIs and the effectiveness of service strategy. Eliminate in-person lines by allowing customers to join a virtual waitlist using their phones. Monitor your line in real-time. Let customers safely wait in their car, at home, or outside. Notify them when you are ready to serve them. Give customers regular updates and wait times. Make them feel like VIPs by talking to them directly and asking for their feedback.
-
5
RecBole
A unified, comprehensive and efficient recommendation library
...We implement more than 100 commonly used recommendation algorithms and provide formatted copies of 28 recommendation datasets. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms. RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified, comprehensive and efficient framework for research purpose. It can be installed from pip, conda and source, and is easy to use. We have implemented more than 100 recommender system models, covering four common recommender system categories in RecBole and eight toolkits of RecBole2.0, including General Recommendation, Sequential Recommendation, Context-aware Recommendation, and Knowledge-based Recommendation and sub-packages. -
6
AmpliGraph
Python library for Representation Learning on Knowledge Graphs
Open source library based on TensorFlow that predicts links between concepts in a knowledge graph. AmpliGraph is a suite of neural machine learning models for relational Learning, a branch of machine learning that deals with supervised learning on knowledge graphs. -
7
MONAI
AI Toolkit for Healthcare Imaging
The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm. Project MONAI also includes MONAI Label, an intelligent open source image labeling and learning tool that helps researchers and clinicians collaborate, create... -
8
StatsForecast
Fast forecasting with statistical and econometric models
...Its internal implementation leverages numba to compile performance-critical code to optimized machine-level instructions, which makes the models much faster than many traditional Python counterparts. -
9
TorchMetrics
Machine learning metrics for distributed, scalable PyTorch application
...Automatic synchronization between multiple devices. Metric arithmetic. Similar to torch.nn, most metrics have both a module-based and a functional version. The functional versions are simple python functions that as input take torch.tensors and return the corresponding metric as a torch.tensor. -
Workable Hiring Software - Hire The Best People, FastWorkable is the preferred software for today's recruiting industry and HR teams, trusted by over 6,000 companies to streamline their hiring processes. Finding the right person for the job has never been easier—users now possess the ability to manage multiple hiring pipelines at once, from posting a job to sourcing candidates. Workable is also seamlessly integrated between desktop and mobile, allowing admins full control and flexibility all in the ATS without needing additional software.
-
10
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... -
11
Label Studio
Label Studio is a multi-type data labeling and annotation tool
The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can... -
12
MiniSom
MiniSom is a minimalistic implementation of the Self Organizing Maps
MiniSom is a minimalistic and Numpy-based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. Minisom is designed to allow researchers to easily build on top of it and to give students the ability to quickly grasp its details. The project initially aimed for a minimalistic implementation of... -
13
NGBoost
Natural Gradient Boosting for Probabilistic Prediction
ngboost is a Python library that implements Natural Gradient Boosting, as described in "NGBoost: Natural Gradient Boosting for Probabilistic Prediction". It is built on top of Scikit-Learn and is designed to be scalable and modular with respect to the choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck. -
14
TensorFlow Privacy
Library for training machine learning models with privacy for data
Library for training machine learning models with privacy for training data. This repository contains the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided. -
15
Advanced Solutions Lab
This repos contains notebooks for the Advanced Solutions Lab
This repository contains Jupyter notebooks meant to be run on Vertex AI. This is maintained by Google Cloud’s Advanced Solutions Lab (ASL) team. Vertex AI is the next-generation AI Platform on the Google Cloud Platform. The material covered in this repo will take a software engineer with no exposure to machine learning to an advanced level. -
16
zvt
Modular quant framework
For practical trading, a complex algorithm is fragile, a complex algorithm building on a complex facility is more fragile, complex algorithm building on a complex facility by a complex team is more and more fragile. zvt wants to provide a simple facility for building a straightforward algorithm. Technologies come and technologies go, but market insight is forever. Your world is built by core concepts inside you, so it’s you. zvt world is built by core concepts inside the market, so it’s zvt.... -
17
Netron
Visualizer for neural network, deep learning, machine learning models
...Netron has experimental support for TensorFlow, PyTorch, TorchScript, OpenVINO, Torch, Arm NN, BigDL, Chainer, CNTK, Deeplearning4j, MediaPipe, ML.NET, scikit-learn, TensorFlow.js. There is an extense variety of sample model files to download or open using the browser version. It is supported by macOS, Windows, Linux, Python Server and browser. -
18
Fairlearn
A Python package to assess and improve fairness of ML models
Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage. -
19
Causal ML
Uplift modeling and causal inference with machine learning algorithms
Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research [1]. It provides a standard interface that allows users to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data. -
20
ChatterBot
Machine learning, conversational dialog engine for creating chat bots
ChatterBot is a Python library that makes it easy to generate automated responses to a user’s input. ChatterBot uses a selection of machine learning algorithms to produce different types of responses. This makes it easy for developers to create chat bots and automate conversations with users. For more details about the ideas and concepts behind ChatterBot see the process flow diagram. -
21
Darts
A python library for easy manipulation and forecasting of time series
...The ML-based models can be trained on potentially large datasets containing multiple time series, and some of the models offer a rich support for probabilistic forecasting. We recommend to first setup a clean Python environment for your project with at least Python 3.7 using your favorite tool (conda, venv, virtualenv with or without virtualenvwrapper). -
22
NVIDIA NeMo
Toolkit for conversational AI
NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI... -
23
TorchAudio
Data manipulation and transformation for audio signal processing
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch... -
24
Alibi Explain
Algorithms for explaining machine learning models
Alibi is a Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. -
25
KerasTuner
A Hyperparameter Tuning Library for Keras
KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search...
