seondong Goto Github PK

2016_cs224d

Self-study CS224d lecture by stanford (http://cs224d.stanford.edu/assignment1/index.html)

2048

A small clone of 1024 (https://play.google.com/store/apps/details?id=com.veewo.a1024)

2048-ai

AI for the 2048 game

academicpages.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

advanced_ml

agdistis

AGDISTIS - Agnostic Named Entity Disambiguation

algospot

my algospot solutions

arc-visualizations.github.io

arduinocolor

A simplified version of the Color Namer app from Katie Dektar (https://play.google.com/store/apps/details?id=com.color.colornamer&hl=en) for demonstrating Android-Arduino communication with RGB LEDs

autostreamlit

awesome-active-learning

A list of active learning methods for deep learning.

awesome-active-learning-1

Awesome Active Learning Paper List

awesome-active-learning-2

A curated list of awesome Active Learning

awesome-anomaly-detection

A curated list of awesome anomaly detection resources

awesome-anomaly-detection-1

A complete list of papers on anomaly detection.

awesome-fraud-detection-papers

A curated list of data mining papers about fraud detection.

awesome-time-series

Resources for working with time series and sequence data

awesome_lists

Awesome Lists for Tenure-Track Assistant Professors and PhD students. (助理教授/博士生生存指南)

bboxveryold

bidir-synth

carpedm20.github.com

clt21

cs231n.github.io

Public facing notes page

ctgan

Conditional GAN for Tabular Data

cuda-convnet

started from Alex's code on google code

customs-declaration-datasets

Customs Import Declaration Datasets

customs-fraud-detection

Simulation framework for customs fraud detection using import declarations.

dcai-lab

Lab assignments for Introduction to Data-Centric AI, MIT IAP 2023 👩🏽‍💻

designing-ml-systems-summary

A detailed summary of "Designing Machine Learning Systems" by Chip Huyen. This book gives you and end-to-end view of all the steps required to build AND OPERATE ML products in production. It is a must-read for ML practitioners and Software Engineers Transitioning into ML.

discrete_event_modeling

By python Simpy