deep-learning

NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models

Easiest way to use AI models without coding (Web UI & API support)

Code for Deep Learning for Modern AI

Free AI/ML course with 950+ Jupyter notebooks — Python, deep learning, LLMs, RAG, agents, prompt engineering, fine-tuning, MLOps

[CVPR'22] Official PyTorch Implementation of "Collaborative Transformers for Grounded Situation Recognition"

A collection of AI/ML articles, guides and projects.

A deep learning model that can create high quality images by training on a dataset.

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage deep learning and deep transfer learning to solve popular tasks in NLP including Classification, Information Retrieval, Sentiment Analysis, Search Engines, Clustering, Paraphrase Mining, Summarization, Language Translation, Q&A systems

Fastest Diffusion backend, WebUI, server. Pushing implementation and discovery of optimizations techniques 🚀

Train Llama 3 models from scratch. Any scale, any personality. By Arianna Method.

A lightweight but powerful library to build token indices for NLP tasks, compatible with major Deep Learning frameworks like PyTorch and Tensorflow.

Code snippets and solutions for the Introduction to Deep Learning and Neural Networks Course hosted in educative.io

Physics-inspired transformer modules based on mean-field dynamics of vector-spin models in JAX

[TITS] reliable dense matching based point cloud registration for autonomous driving

A one-stop library to standardize the inference and evaluation of all the conditional video generation models.

Interpretable Pre-Trained Transformers for Heart Time-Series Data

This repository contain my 75Day Hard Generative AI and LLM Learning Challenge.

Human parsing model for fashion and virtual try-on applications

Toy-scale unified multimodal model experiments — encoder-free understanding & generation with Mixture-of-Transformers on MLX/Apple Silicon

A Unified Framework for Benchmarking Generative Electrocardiogram-Language Models (ELMs)