DanielF29

DanielF29's Stars

• labmlai/annotated_deep_learning_paper_implementations

  🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

  Language:Python59.5k4731366k

• mlabonne/llm-course

  Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

  Language:Jupyter Notebook48.6k476765.2k

• Genesis-Embodied-AI/Genesis

  A generative world for general-purpose robotics & embodied AI learning.

  Language:Python24.6k2315462.1k

• stanford-oval/storm

  An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.

  Language:Python23.6k1711972.1k

• stanfordnlp/dspy

  DSPy: The framework for programming—not prompting—language models

  Language:Python22.7k1521k1.7k

• pubkey/rxdb

  A fast, local first, reactive Database for JavaScript Applications https://rxdb.info/

  Language:TypeScript22.2k2097791.1k

• huggingface/lerobot

  🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning

  Language:Python11.1k1072651.2k

• py-why/dowhy

  DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

  Language:Python7.4k139489951

• frdel/agent-zero

  Agent Zero AI framework

  Language:Python6.4k1862231.4k

• MooreThreads/Moore-AnimateAnyone

  Character Animation (AnimateAnyone, Face Reenactment)

  Language:Python3.4k36157262

• MAIF/shapash

  🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

  Language:Jupyter Notebook2.9k36230343

• midudev/javascript-100-proyectos

  100 proyectos de JavaScript con código y vídeos. ¡Gratis!

  Language:HTML2.4k306269

• remote-es/remotes

  This is a repository listing companies which offer full-time remote jobs with Spanish contracts

  2.4k13829208

• understandable-machine-intelligence-lab/Quantus

  Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

  Language:Jupyter Notebook5941013576

• bethgelab/imagecorruptions

  Python package to corrupt arbitrary images.

  Language:Python42671671

• j-w-yun/optimizer-visualization

  Visualize Tensorflow's optimizers.

  Language:Python3838454

• Nicolopez603/recursos-testing

  Vamos a encontrar multiples recursos para nuestro camino como Testers

  27817145

• midudev/pokedex-for-ci

  Example project for explaining CI

  Language:JavaScript12330603

• CG80499/KAN-GPT-2

  Training small GPT-2 style models using Kolmogorov-Arnold networks.

  Language:Python115337

• Sentdex/neural-net-internals-visualized

  Visualizing some of the internals of a neural network during training and inference.

  Language:Jupyter Notebook73209

• usail-hkust/Awesome-Causality-Inspired-GNNs

  An awesome collection of causality-inspired graph neural networks.

  69106

• amjadraza/pandasai-app-gradio

  Language:Python57106

• JACantoral/DL_fundamentals

  Videos from my YouTube channel about Deep Learning | Videos de mi canal de YouTube acerca de Fundamentos de Deep Learning

  Language:Jupyter Notebook385033

• xmq1221/awesome-prototype-based-papers

  This is a list of awesome prototype-based papers for explainable artificial intelligence.

  36101

• HipGraph/GNNShap

  [The Web Conference 2024] GNNShap: Scalable and Accurate GNN Explanation using Shapley Values

  Language:Python30217

• ITESM-MNA/MLOps

  Language:Jupyter Notebook234016

• gianlucarloni/causality_conv_nets

  Experiment with our attention-inspired framework for causality-driven CNNs: learn how to model causal dispositions within image datasets and enhance your image classifier's performance and XAI robustness via our causality-factors extractor.

  Language:Python5201

• Ivanrs297/endoscopycorruptions

  The endoscopycorruptions Python package provides utilities to simulate common image corruptions that might occur during endoscopic procedures. This tool is designed to assist in the development and testing of image processing algorithms intended for endoscopic imagery by introducing realistic corruptions into clean images.

  Language:Jupyter Notebook5100

• chseifert/xai-papers

  Language:TypeScript1000

• Ivanrs297/awesome-public-datasets

  A topic-centric list of HQ open datasets.

  1000