jbulow
Follow
AI/ML
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Flax is a neural network library for JAX that is designed for flexibility.
Panel: The powerful data exploration & web app framework for Python
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
PyNeuraLogic lets you use Python to create Differentiable Logic Programs
Merlion: A Machine Learning Framework for Time Series Intelligence
Getting the latest versions of Disco Diffusion to work locally, instead of colab. Including how I run this on Windows, despite some Linux only dependencies ;)
Pytorch implementation of Multi-Object Network(MONet)
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
ncnn is a high-performance neural network inference framework optimized for the mobile platform
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
A highly optimised C++ library for mathematical applications and neural networks.
Probabilistic time series modeling in Python
Port of OpenAI's Whisper model in C/C++
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
Development repository for the Triton language and compiler
🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily conf…
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch
Pythonic AI generation of images and videos
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
Compute fractional differentiation super-fast. Processes time-series to be stationary while preserving memory. cf. "Advances in Financial Machine Learning" by M. Prado.
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)