Search Results for "model-builder"
Sort By:
139 projects for "model-builder" with 2 filters applied:
-
Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
-
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.
-
1
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. -
2
MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
3
TabPFN
Foundation Model for Tabular Data
TabPFN is an open-source machine learning system that introduces a foundation model designed specifically for tabular data analysis. The model is based on transformer architectures and implements a prior-data fitted network that can perform supervised learning tasks such as classification and regression with minimal configuration. Unlike many traditional machine learning workflows that require extensive hyperparameter tuning and training cycles, TabPFN is pre-trained to perform inference directly on tabular datasets. ... -
4
Responsible AI Toolbox
Responsible AI Toolbox is a suite of tools providing model
Responsible AI Toolbox is a software framework designed to help developers evaluate and improve the reliability, fairness, and transparency of machine learning systems. The project provides tools that assist in analyzing model behavior, detecting bias, improving robustness, and explaining predictions produced by AI systems. It is designed to integrate with common machine learning frameworks, especially PyTorch, allowing developers to apply responsible AI techniques within existing workflows. The toolbox includes methods for adversarial testing, interpretability analysis, and model diagnostics that help developers understand how models behave under different conditions. ... -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
-
5
seq2seq-couplet
Play couplet with seq2seq model
seq2seq-couplet is a deep learning application that generates Chinese couplet responses using a sequence-to-sequence model built with TensorFlow. Its purpose is not general machine translation, but a specialized text generation task in which the model produces a matching second line for a given first line in the style of traditional couplets. The repository includes the code needed to train the model, configure file paths and hyperparameters, and evaluate progress through loss and BLEU score tracking. ... -
6
Interactive Machine Learning Experiments
Interactive Machine Learning experiments
...Because the project focuses on experimentation rather than production systems, it acts as a sandbox where developers can explore machine learning concepts and observe model behavior. The notebooks reveal how each model is trained and provide opportunities to modify parameters or datasets to observe different outcomes. -
7
Watermark-Removal
Machine learning image inpainting task that removes watermarks
...The project uses neural network models inspired by research in contextual attention and gated convolution, which are methods commonly applied to image restoration tasks. 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. -
8
RF-DETR
RF-DETR is a real-time object detection and segmentation
RF-DETR is an open-source computer vision framework that implements a real-time object detection and instance segmentation model based on transformer architectures. Developed by Roboflow, the project builds upon modern vision transformer backbones such as DINOv2 to achieve strong accuracy while maintaining efficient inference speeds suitable for real-time applications. The model is designed to detect objects and segment them within images or video streams using a unified detection pipeline. ... -
9
Kodezi Chronos
Kodezi Chronos is a debugging-first language model
...Another component, Persistent Debug Memory, allows the system to learn patterns from past debugging sessions and apply that knowledge to future problems. The repository mainly contains research documentation, evaluation benchmarks, and experimental frameworks rather than the full proprietary model implementation. -
Compliant and Reliable File Transfers Backed by Top Security CertificationsStop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
-
10
machine learning tutorials
machine learning tutorials (mainly in Python3)
...The content is organized into multiple sections covering topics such as clustering, regression, dimensionality reduction, recommender systems, and model evaluation. -
11
Diffusion for World Modeling
Learning agent trained in a diffusion world model
Diffusion for World Modeling is an experimental reinforcement learning system that trains intelligent agents inside a simulated environment generated by a diffusion-based world model. The project introduces the idea of using diffusion models, commonly used for image generation, to simulate the dynamics of an environment and predict future states based on previous observations and actions. Instead of interacting directly with a real environment, the reinforcement learning agent learns within a generative model that produces frames representing the environment. ... -
12
mlforecast
Scalable machine learning for time series forecasting
...Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates those automatically based on a simple configuration. It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. The library is built to scale: behind the scenes, it can leverage distributed computing frameworks (Spark, Dask, Ray) when datasets or the number of series grow large. -
13
shimmy
Python-free Rust inference server
...Shimmy focuses on performance and simplicity, using efficient runtime components to minimize memory usage and startup time compared to heavier inference frameworks. It supports modern model formats such as GGUF and SafeTensors and can automatically discover models stored locally or in common directories used by other AI tools. Advanced capabilities include CPU offloading for Mixture-of-Experts models and GPU acceleration, enabling large models to run on consumer hardware with limited VRAM. -
14
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 both experimentation and production-scale development. ... -
15
skfolio
Python library for portfolio optimization built on top of scikit-learn
...The project provides a unified machine learning-style framework for building, validating, and comparing portfolio allocation strategies using financial data. By following the familiar scikit-learn API design, the library allows quantitative researchers and developers to apply techniques such as model selection, cross-validation, and hyperparameter tuning to portfolio construction workflows. It supports a wide range of allocation methods, from classical mean-variance optimization to modern techniques that rely on clustering, factor models, and risk-based allocations. The framework also includes tools for evaluating portfolio performance under different market conditions, enabling users to test robustness and reduce the risk of overfitting. -
16
SimpleTuner
A general fine-tuning kit geared toward image/video/audio diffusion
...It supports fine-tuning workflows for models such as Stable Diffusion variants and other diffusion architectures, enabling users to adapt pretrained models to specialized datasets or creative tasks. The system includes configuration-driven training processes that allow users to define datasets, model paths, and training parameters with minimal setup. SimpleTuner also emphasizes experimentation and academic collaboration, encouraging contributions and iterative improvements from the open-source community. -
17
TimeMixer
Decomposable Multiscale Mixing for Time Series Forecasting
TimeMixer is a deep learning framework designed for advanced time series forecasting and analysis using a multiscale neural architecture. The model focuses on decomposing time series data into multiple temporal scales in order to capture both short-term seasonal patterns and long-term trends. Instead of relying on traditional recurrent or transformer-based architectures, TimeMixer is implemented as a fully multilayer perceptron–based model that performs temporal mixing across different resolutions of the data. ... -
18
rust-bert
Rust native ready-to-use NLP pipelines and transformer-based models
...It allows developers to run state-of-the-art NLP models like BERT, GPT-2, and DistilBERT directly within Rust applications while maintaining high performance and memory efficiency. The library integrates with Rust machine learning infrastructure using crates such as tch-rs and ONNX Runtime for model execution. It also includes tokenization utilities, model architectures, and task-specific pipelines that simplify the development of NLP applications. Because Rust is known for its safety and performance, this project enables developers to deploy modern NLP models in production systems written in Rust. -
19
Machine Learning Study
This repository is for helping those interested in machine learning
...The project compiles notebooks, explanatory documents, and practical code examples that illustrate common machine learning workflows. Topics covered include supervised learning algorithms, feature engineering, model training, and performance evaluation techniques. The repository is structured as a learning resource that guides readers through building machine learning models step by step. It often demonstrates how to implement algorithms using widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Many examples include dataset preparation, visualization of results, and experimentation with different modeling approaches. -
20
Learn PyTorch for Deep Learning
Materials for the Learn PyTorch for Deep Learning
...The materials include Jupyter notebooks, explanations of core deep learning concepts, and step-by-step demonstrations of building and training neural networks. Throughout the lessons, users learn how to work with tensors, create neural network architectures, manage training workflows, and evaluate model performance. -
21
Koila
Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code
...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. -
22
OpenVINO Notebooks
Jupyter notebook tutorials for OpenVINO
openvino_notebooks is a collection of interactive Jupyter notebooks designed to demonstrate how to build, optimize, and deploy artificial intelligence applications using the OpenVINO toolkit. The repository provides practical tutorials that guide developers through various AI workflows including computer vision, natural language processing, and generative AI tasks. Each notebook demonstrates how to run pre-trained models, optimize inference performance, and deploy models across hardware such... -
23
thorough-pytorch
PyTorch Getting Started Tutorial, read online
...The project encourages collaborative learning and often organizes materials in a step-by-step progression that gradually increases in complexity. Topics include neural network fundamentals, training procedures, model evaluation, and practical deep learning workflows. By combining structured lessons with programming projects, the repository aims to help learners develop both conceptual understanding and practical implementation skills. -
24
handson-ml3
Fundamentals of Machine Learning and Deep Learning
...It guides readers through modern machine learning and deep learning workflows using Python, with examples spanning data preparation, supervised and unsupervised learning, deep neural networks, RL, and production-ready model deployment. The third edition updates the content for TensorFlow 2 and Keras, introduces new chapters (for example on reinforcement learning or generative models), and offers best-practice code that reflects current ecosystems. The notebooks are designed so you can run them locally or on Colab/online, making it accessible for learners regardless of infrastructure. ... -
25
Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
...The repository includes datasets, Jupyter notebooks, documentation, and example code that walk learners through the entire machine learning workflow from problem definition to model deployment. The course introduces essential tools such as NumPy, pandas, Matplotlib, and scikit-learn before moving on to deep learning with frameworks like TensorFlow and Keras. It also includes milestone projects that demonstrate how to build end-to-end machine learning systems using real datasets, including classification and regression tasks.
