Search Results for "qr-code-generator" - Page 2
Sort By:
Showing 221 open source projects for "qr-code-generator"
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.
-
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.
-
1
Koila
Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code
...This approach enables developers to experiment with larger batch sizes and more complex architectures while maintaining stable training behavior. The system acts as a thin wrapper around PyTorch tensors and operations, meaning that it integrates easily into existing PyTorch code without requiring major changes to model implementations. It is particularly useful in environments where GPU resources are limited or where models frequently encounter CUDA memory errors. -
2
MiniSom
MiniSom is a minimalistic implementation of the Self Organizing Maps
...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 the Self-Organizing Map (SOM) algorithm, focusing on simplicity in features, dependencies, and code style. Although it has expanded in terms of features, it remains minimalistic by relying only on the numpy library and emphasizing vectorization in coding style. -
3
RL with PyTorch
Clean, Robust, and Unified PyTorch implementation
...The project focuses on helping developers and researchers understand reinforcement learning methods by providing clean and reproducible implementations of well-known algorithms. It includes code for popular deep reinforcement learning techniques such as Deep Q-Networks, policy gradient methods, actor-critic architectures, and other modern RL approaches. The repository is structured so that users can easily experiment with different algorithms and training environments. Many examples demonstrate how agents learn to interact with simulated environments through trial and error using reinforcement learning principles. ... -
4
Data Science Articles from CodeCut
Collection of useful data science topics along with articles
...The materials address areas such as MLOps, data management, project organization, testing practices, visualization techniques, and productivity tools used by data scientists. Each topic often includes references to code repositories, demonstrations, and video tutorials that show how the tools can be applied in real projects. The repository is intended to help practitioners stay updated with current best practices and technologies in the field of data science. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
5
Optax
Optax is a gradient processing and optimization library for JAX
...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 readability and structuring code to match standard equations, over code reuse. -
6
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. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harm. Fairness of AI systems is about more than simply running lines of code. ... -
7
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. -
8
Google Research: Language
Shared repository for open-sourced projects from the Google AI Lang
...These implementations often explore advanced techniques such as language modeling, semantic understanding, information retrieval, and multilingual text processing. The repository functions as a collaborative hub where different research initiatives can publish their code, enabling the broader community to reproduce experiments and build upon published work. -
9
machine-learning-refined
Master the fundamentals of machine learning, deep learning
...The project accompanies a series of textbooks and teaching materials that focus on making machine learning concepts accessible through visual demonstrations and simple code implementations. Instead of presenting algorithms purely through mathematical derivations, the repository emphasizes geometric intuition, visualization, and step-by-step experimentation. It includes Jupyter notebooks and scripts that illustrate core machine learning topics such as regression, classification, optimization methods, and neural networks. ... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
10
segment-geospatial
A Python package for segmenting geospatial data with the SAM
...My primary objective is to simplify the process of leveraging SAM for geospatial data analysis by enabling users to achieve this with minimal coding effort. I have adapted the source code of segment-geospatial from the segment-anything-eo repository, and credit for its original version goes to Aliaksandr Hancharenka. -
11
TPOT
A Python Automated Machine Learning tool that optimizes ML
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. -
12
PyTorch Ignite
Library to help with training and evaluating neural networks
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. Less code than pure PyTorch while ensuring maximum control and simplicity. Library approach and no program's control inversion. Use ignite where and when you need. Extensible API for metrics, experiment managers, and other components. The cool thing with handlers is that they offer unparalleled flexibility (compared to, for example, callbacks). -
13
machine learning tutorials
machine learning tutorials (mainly in Python3)
machine-learning is a continuously updated repository documenting the author’s learning journey through data science and machine learning topics using practical tutorials and experiments. The project presents educational notebooks that combine mathematical explanations with code implementations using Python’s scientific computing ecosystem. Topics covered include classical machine learning algorithms, deep learning models, reinforcement learning, model deployment, and time-series analysis. The repository integrates numerous popular machine learning frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, and Hugging Face. ... -
14
Kaggle Solutions
Collection of Kaggle Solutions and Ideas
Kaggle Solutions is an open-source repository that compiles winning solutions, insights, and educational resources from hundreds of Kaggle data science competitions. The repository acts as a knowledge base for competitive machine learning by collecting solution write-ups, discussion threads, code notebooks, and tutorial resources shared by top Kaggle participants. Each competition entry typically includes information about the dataset, evaluation metrics, modeling strategies, and techniques used by high-ranking competitors. The repository also highlights important machine learning concepts such as feature engineering, cross-validation strategies, ensemble modeling, and post-processing methods commonly used in winning solutions. ... -
15
Avalanche
End-to-End Library for Continual Learning based on PyTorch
Avalanche is an end-to-end Continual Learning library based on Pytorch, born within ContinualAI with the unique goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms. Avalanche can help Continual Learning researchers in several ways. This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major... -
16
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. -
17
Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices. ... -
18
Materials Discovery: GNoME
AI discovers 520000 stable inorganic crystal structures for research
Materials Discovery (GNoME) is a large-scale research initiative by Google DeepMind focused on applying graph neural networks to accelerate the discovery of stable inorganic crystal materials. The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the... -
19
Watermark-Removal
Machine learning image inpainting task that removes watermarks
...Through these techniques, the model learns to identify regions of the image affected by the watermark and generate realistic replacements for the missing visual information. The repository contains code for preprocessing images, training the model, and running inference on images to automatically remove watermark artifacts. -
20
autoresearch
AI agents autonomously run and improve ML experiments overnight
autoresearch is an experimental framework that enables AI agents to autonomously conduct machine learning research by iteratively modifying and training models. Created by Andrej Karpathy, the project allows an agent to edit the model training code, run short experiments, evaluate results, and repeat the process without human intervention. Each experiment runs for a fixed five-minute training window, enabling rapid iteration and consistent comparison across architectural or hyperparameter changes. The system centers on a simple workflow where the agent modifies a single training file while human researchers guide the process through a program.md instruction file. ... -
21
Petastorm
Petastorm library enables single machine or distributed training
...It can also be used from pure Python code. A dataset created using Petastorm is stored in Apache Parquet format. On top of a Parquet schema, petastorm also stores higher-level schema information that makes multidimensional arrays into a native part of a petastorm dataset. Petastorm supports extensible data codecs. These enable a user to use one of the standard data compressions (jpeg, png) or implement her own. -
22
marimo
A reactive notebook for Python
marimo is an open-source reactive notebook for Python, reproducible, git-friendly, executable as a script, and shareable as an app. marimo notebooks are reproducible, extremely interactive, designed for collaboration (git-friendly!), deployable as scripts or apps, and fit for modern Pythonista. Run one cell and marimo reacts by automatically running affected cells, eliminating the error-prone chore of managing the notebook state. marimo's reactive UI elements, like data frame GUIs and plots,... -
23
OpenCLIP
An open source implementation of CLIP
...Specifically, a ResNet-50 model trained with our codebase on OpenAI's 15 million image subset of YFCC achieves 32.7% top-1 accuracy on ImageNet. OpenAI's CLIP model reaches 31.3% when trained on the same subset of YFCC. For ease of experimentation, we also provide code for training on the 3 million images in the Conceptual Captions dataset, where a ResNet-50x4 trained with our codebase reaches 22.2% top-1 ImageNet accuracy. This codebase is work in progress, and we invite all to contribute in making it more accessible and useful. In the future, we plan to add support for TPU training and release larger models. ... -
24
DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. With just a single GPU,... -
25
Thinc
A refreshing functional take on deep learning
...Develop faster and catch bugs sooner with sophisticated type checking. Trying to pass a 1-dimensional array into a model that expects two dimensions? That’s a type error. Your editor can pick it up as the code leaves your fingers.
