A comprehensive and up-to-date developer education platform for Urbit.

Overview

curriculum

A comprehensive and up-to-date developer education platform for Urbit.

This project organizes developer capabilities into a hierarchy of competencies (“objectives”), competency clusters (“lessons”), and ultimately developer education paths.

Given a particular target capability, we can answer the question of exactly what parts of the system you need to learn to be competent at your task.

More or less, we consider the world of Urbit developers to fall into six levels:

  1. Tyro
  2. Amateur
  3. Workaday
  4. Master
  5. Wizard
  6. Client-side

We organize curriculum into series which describe the target transformation:

  1. 100 Tyro→Amateur
  2. 200 Amateur→Workaday
  3. 300 Workaday→Master
  4. 400 Master→Wizard
  5. 500 Wizard++

Particular paths can be traced through the competency cluster dependencies:

I am currently working on making this more legible, and ultimately will use this to rework Hoon School, Hoon 101, and organize access to recommended study materials and documentation.


Next steps:

  • map all runes into this scheme (mostly done, but some runes are never covered, need to decide on this)
  • produce summative assessments for each objective
  • produce formative assessments for each lesson
  • collate/produce content
    • mark every tutorial with Objectives/Key Points

Owner
Sigilante
Sigilante
Implementation for paper "STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement" (ICCV 2021).

STAR-pytorch Implementation for paper "STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement" (ICCV 2021). CVF (pdf) STAR-DC

43 Dec 21, 2022
A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning

LABES This is the code for EMNLP 2020 paper "A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised L

17 Sep 28, 2022
[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

Context Encoders: Feature Learning by Inpainting CVPR 2016 [Project Website] [Imagenet Results] Sample results on held-out images: This is the trainin

Deepak Pathak 829 Dec 31, 2022
Torchlight2 lan game server tool - A message forwarding tool for Torchlight 2 lan game

Torchlight 2 Lan Game Server Tool A message forwarding tool for Torchlight 2 lan

Huaijun Jiang 3 Nov 01, 2022
Code for Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021)

Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021) Hang Zhou, Yasheng Sun, Wayne Wu, Chen Cha

Hang_Zhou 628 Dec 28, 2022
Audio Visual Emotion Recognition using TDA

Audio Visual Emotion Recognition using TDA RAVDESS database with two datasets analyzed: Video and Audio dataset: Audio-Dataset: https://www.kaggle.com

Combinatorial Image Analysis research group 3 May 11, 2022
Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.

mtomo Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation.

Katsuya Hyodo 24 Mar 02, 2022
1st place solution in CCF BDCI 2021 ULSEG challenge

1st place solution in CCF BDCI 2021 ULSEG challenge This is the source code of the 1st place solution for ultrasound image angioma segmentation task (

Chenxu Peng 30 Nov 22, 2022
Citation Intent Classification in scientific papers using the Scicite dataset an Pytorch

Citation Intent Classification Table of Contents About the Project Built With Installation Usage Acknowledgments About The Project Citation Intent Cla

Federico Nocentini 4 Mar 04, 2022
GLIP: Grounded Language-Image Pre-training

GLIP: Grounded Language-Image Pre-training Updates 12/06/2021: GLIP paper on arxiv https://arxiv.org/abs/2112.03857. Code and Model are under internal

Microsoft 862 Jan 01, 2023
This code is a toolbox that uses Torch library for training and evaluating the ERFNet architecture for semantic segmentation.

ERFNet This code is a toolbox that uses Torch library for training and evaluating the ERFNet architecture for semantic segmentation. NEW!! New PyTorch

Edu 104 Jan 05, 2023
A PyTorch-based library for fast prototyping and sharing of deep neural network models.

A PyTorch-based library for fast prototyping and sharing of deep neural network models.

78 Jan 03, 2023
Decensoring Hentai with Deep Neural Networks. Formerly named DeepMindBreak.

DeepCreamPy Decensoring Hentai with Deep Neural Networks. Formerly named DeepMindBreak. A deep learning-based tool to automatically replace censored a

616 Jan 06, 2023
Unified tracking framework with a single appearance model

Paper: Do different tracking tasks require different appearance model? [ArXiv] (comming soon) [Project Page] (comming soon) UniTrack is a simple and U

ZhongdaoWang 300 Dec 24, 2022
Code for "Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation". [AAAI 2021]

Graph Evolving Meta-Learning for Low-resource Medical Dialogue Generation Code to be further cleaned... This repo contains the code of the following p

Shuai Lin 29 Nov 01, 2022
Applying PVT to Semantic Segmentation

Applying PVT to Semantic Segmentation Here, we take MMSegmentation v0.13.0 as an example, applying PVTv2 to SemanticFPN. For details see Pyramid Visio

35 Nov 30, 2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li

DamoCV 25 Dec 16, 2022
A python module for scientific analysis of 3D objects based on VTK and Numpy

A lightweight and powerful python module for scientific analysis and visualization of 3d objects.

Marco Musy 1.5k Jan 06, 2023
Source code for paper "ATP: AMRize Than Parse! Enhancing AMR Parsing with PseudoAMRs" @NAACL-2022

ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs Hi this is the source code of our paper "ATP: AMRize Then Parse! Enhancing AMR Parsing w

Chen Liang 13 Nov 23, 2022
Easy Parallel Library (EPL) is a general and efficient deep learning framework for distributed model training.

English | 简体中文 Easy Parallel Library Overview Easy Parallel Library (EPL) is a general and efficient library for distributed model training. Usability

Alibaba 185 Dec 21, 2022