Projects sharing features with DeepLearningProject

nanoVLM is a training framework and toolkit for small vision-language models. It provides a PyTorch-based environment for training and fine-tuning models to associate image inputs with textual descriptions and generate natural language answers. The project includes a cloud model versioning tool for saving and loading model weights to centralized repositories to synchronize assets across environments. It also features a dedicated evaluation suite for measuring the accuracy and reliability of vision-language models against standard task datasets. The framework covers GPU resource planning thro

This project serves as an educational and practical resource for mastering machine learning workflows using Python. It provides a comprehensive collection of code examples and exercises designed to guide users through the implementation of predictive systems, ranging from fundamental algorithms to deep learning architectures. The repository distinguishes itself by offering a structured approach to both classical machine learning and neural network training. It covers the full lifecycle of model development, including the orchestration of reusable data transformation pipelines, advanced ensemb

This project is a comprehensive educational resource and tutorial handbook for building, training, and deploying machine learning models using TensorFlow 2. It serves as a structured learning guide covering core deep learning concepts, including neural network architectures, automatic differentiation, and tensor operations. The handbook provides technical guidance on optimizing execution efficiency through GPU memory management, distributed training, and model quantization. It also includes detailed manuals for constructing high-performance data pipelines and exporting models for production s

MMF is a modular framework for building, training, and evaluating vision-and-language models. It provides a configuration-driven experiment system where model, dataset, and training parameters are defined through composable YAML files, alongside a curated model zoo of pretrained checkpoints for state-of-the-art multimodal architectures. The framework includes a multimodal dataset loader that downloads, processes, and batches vision-and-language data, and a vision-language model trainer supporting distributed training, mixed precision, and checkpoint-based resumption. The framework distinguish

MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate

Edward is a probabilistic programming language and inference engine designed for building deep generative models and Bayesian neural networks. It utilizes the TensorFlow framework to represent probabilistic models as differentiable computational graphs. The library enables the construction of complex data distributions through Bayesian neural networks, mixture models, and Gaussian processes. It differentiates itself by providing an integrated toolkit for both supervised and unsupervised probabilistic modeling, including the implementation of generative adversarial networks and mixture density

This project is an educational resource and step-by-step guide for implementing end-to-end machine learning workflows. It provides a structured walkthrough for managing the entire lifecycle of a predictive modeling project, from initial data cleaning and feature engineering to final model training and performance assessment. The repository utilizes interactive documents to interleave code, data visualizations, and narrative explanations, facilitating a reproducible approach to data science. By following this guided sequence, users can construct and orchestrate pipelines that transform raw dat

PaddleX is a PaddlePaddle-based framework for building, deploying, and fine-tuning AI model pipelines, with pre-built support for computer vision, OCR, document analysis, and time series tasks. It offers a toolkit of ready-to-use pipelines for image classification, object detection, segmentation, and pose estimation, alongside an end-to-end OCR document analysis pipeline that extracts text, tables, formulas, and layout information. The platform also includes a dedicated time series forecasting pipeline for analyzing historical data to detect anomalies, classify patterns, and predict future val

Flashlight is a standalone C++ machine learning library and tensor library used for building and training neural networks. It functions as a comprehensive neural network framework and automatic differentiation engine, providing the tools to construct computation graphs and calculate gradients via backpropagation. The project serves as a distributed training framework, utilizing all-reduce operations to synchronize gradients and parameters across multiple compute nodes and devices. It distinguishes itself through deep integration of high-performance tensor manipulation, native device memory in

Lit is a machine learning interpretability framework and model debugging tool designed to analyze model behavior and performance. It serves as an interpretability dashboard for large language models and a general performance analyzer for text, image, and tabular datasets. The project distinguishes itself through a comprehensive suite of interpretability tools, including salience map generation for feature attribution, the creation of synthetic and counterfactual examples to test robustness, and the projection of high-dimensional embeddings into visual spaces via UMAP or PCA. It further enable

mlpack is a header-only C++ machine learning library that defines matrix types as compile-time templates, enabling flexible numeric precision and memory layout without runtime overhead. Its core identity is built around a template metaprogramming architecture that allows algorithms to be included selectively as independent modules, reducing binary size, and supports compile-time serialization of neural network parameters by deducing matrix types and structure at compile time. The library distinguishes itself through a multi-language binding framework that automatically generates bindings for

This project is a BitTorrent media streamer and peer-to-peer movie player built on Node.js. It functions as a torrent-based video client that delivers media content without requiring full file downloads. The application integrates external movie databases and subtitle repositories to provide a streaming interface. It aggregates film descriptions, cast details, and posters to organize a media library and syncs multi-language captions during playback. The system handles peer-to-peer video playback by streaming content directly from torrent providers in real time.

CogVLM is a multimodal large language model designed to integrate visual and textual data for reasoning about images and generating natural language. It functions as a visual question answering system that analyzes image content to provide detailed descriptions or answer specific questions. The project includes a visual grounding model capable of mapping text descriptions to precise bounding box coordinates within an image. It also features a vision-based automation agent that analyzes screen captures to generate execution plans and interaction coordinates for software interfaces. The system

ml-mgie is a multimodal machine learning framework and image editor designed for instruction-based image manipulation. It utilizes multimodal large language models to translate natural language prompts into precise visual modifications, functioning as a text-to-image editing model. The system is a research implementation focused on aligning visual imagination with textual commands. It employs a training process based on image-pair datasets and descriptive instructions to learn how to execute complex visual edits. The framework covers capabilities in AI-powered visual content creation, includ

This project provides a foundational framework and reference implementation for executing causal language modeling and multimodal reasoning on local systems. It includes a set of core components for managing model assets, a fine-tuning framework, and structural definitions required to instantiate transformer-based architectures. The system is distinguished by its ability to process combined text and image inputs through multimodal transformer models for visual reasoning and document analysis. It also supports the deployment of quantized models, reducing memory footprints through low-precision

This is a Python automated machine learning framework designed to automate the design and optimization of machine learning pipelines. It functions as a genetic programming pipeline optimizer and an automated feature selection tool, using evolutionary search to discover the most effective sequences of data processing and model steps. The project focuses on multi-objective optimization to balance competing performance metrics simultaneously. It employs a genetic selection process to identify impactful variables and remove noise from raw datasets, ensuring the resulting machine learning solution

This project is a PyTorch person re-identification framework designed for training and evaluating models that identify individuals across different camera views. It provides a complete model training pipeline, a deep learning feature extractor for converting images into numeric vectors, and a suite of computer vision benchmarking tools to measure identity retrieval accuracy. The framework includes a specialized transfer learning toolkit that supports layer freezing, staged learning rate optimization, and differential learning rates for fine-tuning pretrained models. It distinguishes itself th

This project is a PyTorch-based deep learning framework and supervised learning baseline for person and vehicle re-identification. It provides a complete pipeline for training and evaluating models designed to extract identity-based feature embeddings and match the same entity across different camera views. The framework distinguishes itself with support for cross-modality identity matching, enabling the retrieval of identities across different imaging sensors such as RGB and infrared. It also includes advanced retrieval refinement through re-ranking techniques, utilizing reciprocal encoding

This project is a comprehensive educational curriculum for learning data science and predictive modeling using the Python programming language. It provides structured instructional material and guides covering supervised learning, unsupervised learning, and neural network design. The curriculum focuses on building, training, and evaluating machine learning models. It includes specific guides for implementing linear regression, decision trees, and support vector machines for predictive analysis, as well as tutorials on designing convolutional and recurrent neural network architectures. The co

This project is a deep learning text classification framework and neural text analysis library. It provides tools for categorizing textual data, adapting large language models through fine-tuning, and treating classification tasks as sequence generation problems using transformer architectures. The framework distinguishes itself through the implementation of ensemble learning, using boosting to combine predictions from multiple architectures to increase accuracy. It also includes a toolkit for fine-tuning pre-trained models via layer updates and the ability to restore model sessions for real-

This project is a structured educational program and machine learning engineering course. It provides a comprehensive curriculum and learning path focused on data science, the development of predictive models, and the operational aspects of MLOps. The instructional material covers the full machine learning lifecycle, moving from basic data engineering to production deployment. This includes guides on wrapping models in APIs, utilizing container-based packaging, and implementing serverless architectures to host models in cloud environments. The program encompasses technical training in predic

Yellowbrick is a machine learning visualization library and model diagnostic tool designed to analyze feature importance, target distributions, and model error metrics. It serves as a visual toolkit for diagnosing underfitting and overfitting through the use of validation and learning curves. The project provides specialized suites for evaluating predictive models and unsupervised learning. It enables the determination of optimal cluster counts via elbow methods and silhouette coefficients, and assesses classifier and regressor quality through ROC curves, confusion matrices, and residual plot

Certd is a self-hosted platform that automates the full lifecycle of SSL certificates using the ACME protocol. It handles certificate application, renewal, and deployment across multiple domains through a pipeline-driven workflow engine, with DNS challenge orchestration and multi-cloud deployment capabilities. The platform distinguishes itself through its configurable pipeline system, which allows users to build multi-step workflows that can pass outputs between tasks, execute custom scripts, and handle errors. It supports multi-tenant access control with role-based permissions, encrypted cre

opencv4nodejs is a set of JavaScript wrappers and a C++ native addon that provides Node.js bindings for the OpenCV library. It functions as a computer vision library and image processing framework, exposing high-performance C++ algorithms to a JavaScript environment. The project enables the execution of vision algorithms for detecting faces, tracking objects, and analyzing visual data using deep neural networks. It includes capabilities for data pattern classification, text pattern recognition, and the identification of facial landmarks and gestures. The framework covers a broad capability s

Lmnr is an LLM observability platform and evaluation framework designed for tracing, logging, and monitoring language model executions. It provides the tools necessary to debug agent behavior, analyze performance, and identify failure patterns in AI agents. The platform differentiates itself through a trace-to-dataset pipeline that converts production logs into labeled test sets for regression testing. It includes a prompt-variant replay engine to compare different prompts or models side-by-side and a state-cached debugging system to replay agent loops without restarting the process. The sys

This project is a Go library designed for facial detection, landmark mapping, and identity verification. It provides a toolkit for integrating computer vision capabilities into applications, enabling the automated identification and analysis of human faces within digital images. The library utilizes a deep residual network to transform facial data into compact vector representations, which are then compared using geometric distance calculations to confirm identities. It employs histogram-based object detection to locate facial structures and maps specific points on the face to define geometry

Mmlspark is a distributed framework for executing machine learning models, data transformations, and AI service integrations across Apache Spark clusters. It functions as a distributed machine learning library and pipeline orchestrator, allowing users to integrate pre-trained cognitive services and custom models into large-scale batch and streaming workflows. The project is distinguished by its ability to incorporate external AI services and web APIs directly into big data pipelines for text and vision analysis. It provides a scalable model training framework that coordinates gradient boostin