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alipy

ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.

bob.c.y.github.io

a repository for my first blogpost

bobchengyang.github.io

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

bobcy.github.io

bss-euclidean_mahalanobis_bayes_classifier

build-a-regression-model-in-keras

final assignment of coursera course https://www.coursera.org/learn/introduction-to-deep-learning-with-keras

cool-looking-data-visualization-examples

cool looking data visualization examples from various places

coursera-ibm-ai-engineering

Partial course materials for Coursera IBM AI Engineering Specialization

coursera_capstone

Hello Capstone Project Course Week 1 Assignment (coursera)

distancemetriclearning

Convex and Nonsmooth Optimization project

gc_

human

Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gaze Tracking, Gesture Recognition

ibmdatascience

Repository of Coursera IBM Data Science Professional Certificate

jupyter-notebooks

Collection of Jupyter Notebooks demoed on https://www.youtube.com/stevesiedata

linear-programming

MATLAB implementations of the simplex method, dual simplex method and sensitivity analysis.

msgl

Model Selection-inspired Graph Learning

msgl_plus

MSGL+

nbviewer

nbconvert as a web service: Render Jupyter Notebooks as static web pages

recommenders

Best Practices on Recommendation Systems

remove-files-in-a-folder

remove files in a folder

sdp_run

SDP_relaxation_unrolled_network

sgml

source code for running experiments in paper https://arxiv.org/abs/2006.08816v6.pdf

some_useful_commands

some useful commands

spectralclustering

Spectral Clustering based on Random Walk (Normalized Laplacian Matrix) (Matlab)

svm-from-scratch

An Implementation of SVM - Support Vector Machines using Linear Kernel. This is just for understanding of SVM and its algorithm.