Stars
II-Agent: a new open-source framework to build and deploy intelligent agents
An automated Endometrial Cancer Diagnosis approach using Histopathological Image through image Preprocessing and Optimized gated Multi-Layer Perceptron Model
A Conversational Speech Generation Model
Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚
The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, MCP compatibility, and more.
A Julia package for real-time audification of ODEs and SDEs
Themeing and convenience tools for accelerating working with Makie.jl
first base model for full-duplex conversational audio
Julia package with several functions to train and analyze Autoencoder-based neural networks
Explorations into the proposal from the paper "Grokfast, Accelerated Grokking by Amplifying Slow Gradients"
Official repository for the paper "Grokfast: Accelerated Grokking by Amplifying Slow Gradients"
A convenient package for working with time series (time-indexed arrays)
Julia toolbox for analyzing neurophysiological data
Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)
Because we don't have enough time to read everything
All the handwritten notes 📝 and source code files 🖥️ used in my YouTube Videos on Machine Learning & Simulation (https://www.youtube.com/channel/UCh0P7KwJhuQ4vrzc3IRuw4Q)
Harnessing Julia's Rich Documentation for Tailored AI-Assisted Coding Guidance
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Multilingual Automatic Speech Recognition with word-level timestamps and confidence
Lectures, code and material for the Modelling and Machine Learning of Dynamical Systems in Julia lecture at Technical University Munich
A collection of tools, helper functions and layers to assist working with chaotic Neural Differential Equation in Julia
This is the source code of the Neural Partial Differential Equations for Chaotic Processes Paper.
Small helper package that provides a struct for sequence learning with Neural ODEs.
Machine Learning for Dynamical Systems Workshop February 23 at TUM