SachiniEMSPE
a student || a newbie ~ test | search | trial-error | ask | learn | read | share ~
NY, USA
SachiniEMSPE's Stars
harvard-edge/cs249r_book
Collaborative book Machine Learning Systems
alphabetakappa/Probabilistic-Graphical-Models-Materials
jona2510/ADforHC
Artificial Datasets for Hierarchical Classification
jona2510/PGM_PyLib
PGM_PyLib: A Python Library for Inference and Learning of Probabilistic Graphical Models
Waikato/meka
Multi-label classifiers and evaluation procedures using the Weka machine learning framework.
node-red/node-red
Low-code programming for event-driven applications
marcobb8/tr_bn
Code of the methods from the PhD thesis "Learning Tractable Bayesian Networks"
kb22/Activity-Recognition-using-Machine-Learning
The project involves training a Machine Learning model to classify the kind of activity a person is performing including sitting, standing, laying, walking, walking upstairs and walking downstairs using data collected from smartphones.
ncullen93/pyBN
Bayesian Networks in Python
andrecamara/weka-android
WEKA Machine Learning framework as an Android Library
rjmarsan/Weka-for-Android
the Weka project with the GUI components removed so it works with Android
dspanah/Human-Activity-Recognition-Keras-Android
Sensor-based human activity recognition from smartphone data in Keras with on-device inference
bnsreenu/python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
aqibsaeed/Human-Activity-Recognition-using-CNN
Convolutional Neural Network for Human Activity Recognition in Tensorflow
ani8897/Human-Activity-Recognition
Classifying the physical activities performed by a user based on accelerometer and gyroscope sensor data collected by a smartphone in the user’s pocket. The activities to be classified are: Standing, Sitting, Stairsup, StairsDown, Walking and Cycling.
networkx/networkx
Network Analysis in Python
ArthurZC23/Machine-Learning-A-Probabilistic-Perspective-Solutions
My solutions to Kevin Murphy Machine Learning Book
2wavetech/Probabilistic-Graphical-Model
This repository contains my implementation of the programming assignments of Probabilistic Graphical Models delivered by Stanford University on Coursera
liang456/Stanford-Probabilistic-Graphical-Models-Coursera
jasonlovescoding/Coursera-ProbabilisticGraphicalModels
The homework assignments finished for the coursera specialization "Probabilistic Graphical Models"
kushagra06/CS228_PGM
🌀 Stanford CS 228 - Probabilistic Graphical Models
shenweichen/Coursera
Quiz & Assignment of Coursera
probml/pml-book
"Probabilistic Machine Learning" - a book series by Kevin Murphy
xunzheng/notears
DAGs with NO TEARS: Continuous Optimization for Structure Learning
walkccc/CLRS
📚 Solutions to Introduction to Algorithms Third Edition
jmschrei/pomegranate
Fast, flexible and easy to use probabilistic modelling in Python.
erdogant/bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
WiringPi/WiringPi
The arguably fastest GPIO Library for the Raspberry Pi
pythonnet/pythonnet
Python for .NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.
adeept/Adeept_Ultimate_Starter_Kit_Python_Code_for_RPi
Adeept Ultimate Starter Kit Python Code for Raspberry Pi