hsianghung Goto Github PK

beer-recommender

code-challenges

cord19recommender

KDD 2020 hands-on tutorial of knowledge graph-based recommender for COVID-19 related research

ctgan

Conditional GAN for generating synthetic tabular data.

data-massage

data-scientist-mooc

Coursera class in data science using R

data_science_coursera

datascienceprojects

deep-learning

deep-learning-book

Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

deeplearningfortimeseriesforecasting

A tutorial demonstrating how to implement deep learning models for time series forecasting

deeprecommender

Deep learning for recommender systems

detext

DeText: A Deep Neural Text Understanding Framework for Ranking and Classification Tasks

geospatial_data_ming

gnn

TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

h2o-meetups

Presentations from H2O meetups & conferences by the H2O.ai team

hyperparameter-optimization

Implementation of Bayesian Hyperparameter Optimization of Machine Learning Algorithms

isolationforest

testing scikit-learn Isolation Forest

kdd-2019-hands-on

DGL tutorial in KDD 2019

kdd20-hands-on-tutorial

Scalable Graph Neural Networks with Deep Graph Library

kdd2019-handson-tutorial

KDD 2019 hands-on tutorial

kdd2020-calibration

How to calibrate your neural network classifier: Getting accurate probabilities from a classification model

labelimg

:metal: LabelImg is a graphical image annotation tool and label object bounding boxes in images

large-language-models-with-semantic-search

Explore from keyword search to dense retrieval and reranking, which injects the intelligence of LLMs into your search system, making it faster and more effective.

llms-in-prod-workshop-2023

Deploy and Scale LLM-based applications

loglizer

A log analysis toolkit for automated anomaly detection [ISSRE'16]

logparser

A toolkit for automated log parsing [ICSE'19, TDSC'18, DSN'16]

machine-learning

machine_learning_note

ml-papers-explained

Explanation to key concepts in ML