mnazaal Goto Github PK

adarl-code

Implementation codes and datasets used in ICLR'22 Spotlight paper AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning.

anki-editor

Emacs minor mode for making Anki cards with Org

archie

A minimal Hugo Theme

artemis

Artemis aims to get rid of all the boring, bureaucratic coding (plotting, file management, organizing experiments, etc) involved in machine learning projects, so you can get to the good stuff quickly.

braindump

knowledge repository managed with org-mode and org-roam.

codebase

MARL-Book's official codebase repo. www.marl-book.com

coms21202-symbols-patterns-and-signals

Coursework on the unit related to introductory topics in data science

coms30127-computational-neuroscience

University of Bristol Computational Neuroscience 2018/19

css-hack-2018

cstree-alg

dibs

Joint Bayesian inference of graph and parameters of general Bayes nets

emacs_config

emat20920-numerical-methods-in-matlab

emukit

A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

enum4linux

enum4Linux is a Linux alternative to enum.exe for enumerating data from Windows and Samba hosts.

forking-workflow-exercise-recorded

Exercise repository used during workshop on stream and recording. Will be removed after workshop.

fuzzy-el

Fuzzy matching utilities for GNU Emacs

hydra-examples

inkml2img

Convert inkml to image

just-the-docs

A modern, high customizable, responsive Jekyll theme for documention with built-in search.

kaggle

llama-cpp.el

A client for llama-cpp server

masters-thesis

mnazaal

notebook-mode

GNU Emacs notebook mode

org-re-reveal

Org export to reveal.js presentations

org-reveal-config

Simplest possible org reveal cofig

org-thesis

Writing a Ph.D. thesis with Org Mode

partia-computing-michaelmas

Activities and exercises for the Part IA computing course in Michaelmas Term

pgmpy

Python Library for learning (Structure and Parameter) and inference (Probabilistic and Causal) in Bayesian Networks.