eddiestudies/jax_privacy
Algorithms for Privacy-Preserving Machine Learning in JAX
Repositories
eddiestudies/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
eddiestudies/dashu
A library set of arbitrary precision numbers implemented in Rust.
eddiestudies/opacus
Training PyTorch models with differential privacy
eddiestudies/dpcourse.github.io
Website for dpcourse
eddiestudies/opendp
The core library of differential privacy algorithms powering the OpenDP Project.
eddiestudies/rlm-self-distill
Large Language Model Self Distillation to Rule based sub task completion
eddiestudies/rlmr
General plug-and-play inference library for Recursive Language Models (RLMs), supporting various sandboxes.
eddiestudies/introtodeeplearning
Lab Materials for MIT 6.S191: Introduction to Deep Learning
eddiestudies/ebook2audiobook
Convert ebooks to audiobooks with chapters and metadata using dynamic AI models and voice cloning. Supports 1,107+ languages!
eddiestudies/FLT
Ongoing Lean formalisation of the proof of Fermat's Last Theorem
eddiestudies/basic-pitch
A lightweight yet powerful audio-to-MIDI converter with pitch bend detection
eddiestudies/salutamitools
Free tools for language learners
eddiestudies/lean
My progression in learning lean4
eddiestudies/modelscribe
eddiestudies/salutnlp
NLP tooling for researchers
eddiestudies/fast-pt
eddiestudies/SumCSE
eddiestudies/guidance
A guidance language for controlling large language models.
eddiestudies/whitenoise-core
Differential privacy validator, runtime and language bindings
eddiestudies/trainingwheels
A debugging library for python
eddiestudies/interpret-text-contrib
A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.
eddiestudies/interpret
Fit interpretable models. Explain blackbox machine learning.
eddiestudies/interpret-community
Fit interpretable models with blackbox machine learning.
eddiestudies/MLADs2019Fall-Interpretability
This is a repo for use in our MLADs Lab in Fall 2019.