Awesome GitHub RepositoriesPositional Encoding Techniques

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

This project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of large language models. It provides a guided progression through the theory, architecture, training, and deployment of these models. The curriculum includes specialized guides on transformer architecture, model training tutorials, and frameworks for designing autonomous agents. It also provides dedicated resources for studying model safety and ethics. The material covers a wide range of technical capabilities, including distributed training strategies, parameter-efficient fine-tu

The Annotated Transformer is an educational resource that provides annotated code implementations of the Transformer architecture for sequence-to-sequence tasks, built with PyTorch. It serves as a learning tool for understanding attention mechanisms, multi-head parallel attention, and scaled dot-product attention through executable examples that walk through each component of the model. The project covers the full Transformer pipeline, including stacked encoder-decoder layers with residual connections and layer normalization, sinusoidal positional encoding for order-aware representation, and

PlugNPlay-Modules is a collection of reusable PyTorch computer vision modules and deep learning architectural components. It provides a library of standardized building blocks for constructing neural networks, focusing on attention mechanisms, signal processing layers, and feature fusion modules. The project is distinguished by its extensive variety of attention primitives, covering spatial, channel, and temporal weighting, as well as specialized variants like deformable, frequency-enhanced, and linear-complexity attention. It also implements advanced signal processing tools within the neural

Este proyecto es una implementación en PyTorch de una red neuronal basada en atención diseñada para tareas de aprendizaje profundo de secuencia a secuencia. Sirve como librería para construir modelos de secuencia de aprendizaje profundo que utilizan estructuras de codificador y decodificador para procesar lenguaje natural y datos secuenciales. La implementación se centra en un mecanismo de atención de múltiples cabezas (multi-head attention) para capturar diversas relaciones entre tokens sin utilizar recurrencia. Incluye codificación posicional sinusoidal para mantener el orden de la secuencia y redes feed-forward puntuales para transformar las posiciones de los tokens de forma independiente. La arquitectura incorpora normalización basada en capas para estabilizar el entrenamiento y acelerar la convergencia. Proporciona los componentes necesarios para el diseño de arquitectura de redes neuronales dentro de los dominios del procesamiento de lenguaje natural y el aprendizaje de secuencia a secuencia.

Tiny Universe is an educational monorepo that delivers multiple independent implementations of core AI subsystems as self-contained Jupyter notebooks. It provides from-scratch constructions of foundational architectures including a complete Transformer model built from the original paper specification, a denoising diffusion probabilistic model for image generation, and a ReAct-style autonomous agent framework that equips an LLM with tools for planning and multi-step task execution. The project distinguishes itself by covering the full lifecycle of modern AI systems through hands-on implementa

DeiT es un framework de vision transformer para PyTorch diseñado para la clasificación de imágenes. Implementa una arquitectura basada en transformers que procesa imágenes como secuencias de parches aplanados utilizando capas de auto-atención (self-attention) y modelado de secuencias consciente de la posición en lugar de filtros convolucionales. El proyecto se centra en el entrenamiento eficiente en datos a través de un framework de destilación de conocimiento. Este sistema permite que un modelo estudiante imite las etiquetas blandas (soft labels) de un modelo profesor de alto rendimiento para mejorar la precisión y la generalización, particularmente cuando se entrena con conjuntos de datos más pequeños. La librería cubre el ciclo de vida completo de desarrollo, incluyendo el entrenamiento de clasificación de imágenes, la optimización de la pérdida de entropía cruzada y el despliegue de pesos preentrenados para inferencia. También incluye una herramienta de benchmarking para evaluar el rendimiento y la precisión del modelo frente a conjuntos de datos estándar.

Neuraloperator is a library for learning mappings between infinite-dimensional function spaces, serving as a tool to accelerate physics simulations and partial differential equation solving. It implements resolution-invariant models and spectral neural networks that can produce consistent predictions regardless of the input grid resolution or spatial discretization. The framework incorporates physics-informed neural networks that enforce physical constraints and differential equations through specialized loss functions. It utilizes Fourier transforms and spectral projections to process multid