Awesome GitHub RepositoriesSinusoidal Encodings

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

Acest proiect este o implementare PyTorch a unei rețele neuronale bazate pe atenție, concepută pentru sarcini de deep learning de tip sequence-to-sequence. Servește drept bibliotecă pentru construirea de modele de secvențe de deep learning care utilizează structuri de encoder și decoder pentru a procesa limbajul natural și datele secvențiale. Implementarea se concentrează pe un mecanism de atenție multi-head pentru a captura relații diverse între token-uri fără a utiliza recurența. Include codificarea pozițională sinusoidală pentru a menține ordinea secvenței și rețele feed-forward punctuale pentru a transforma pozițiile token-urilor în mod independent. Arhitectura încorporează normalizarea bazată pe straturi pentru a stabiliza antrenarea și a accelera convergența. Oferă componentele necesare pentru designul arhitecturii rețelelor neuronale în domeniile procesării limbajului natural și învățării sequence-to-sequence.

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