Bangda Sun

Practice makes perfect

Tags

Analytics Anomaly Detection Attention CTR Clustering Computational Advertising Data Science Data Visualization Decision Tree Deep Learning Elasticsearch Ensemble Algorithms Factorization Models GBDT Kaggle Language Model Linear Algebra Linear Regression Logistic Regression Machine Learning Mathematics Maximum Entropy Model Metrics Model Evaluation NLP Naive Bayes Probability Programming R RNN Recommender Sys SVM Software Development Statistics Time Series Unsupervised Learning Word Embeddings
Analytics

AB Testing (Udacity) Learning Notes (3)

AB Testing (Udacity) Learning Notes (2)

AB Testing (Udacity) Learning Notes (1)

Anomaly Detection

Deep Learning for Anomaly Detection

Time Series Precision Recall

Attention

Summary on Attention in NLP

CTR

DeepFM - Combining FM and Neural Nets

Estimating CTR for New Ads in Search Advertising

Practical Experiences for Ad Click Prediction

A Simple Framework for Predicting CTR in Display Advertising

FFM - More Elaborated and Refined Factorization Machines

Factorization Machine - A General Predictor for Sparse Data

Clustering

Machine Learning Overview Series (8) - Kmeans

Computational Advertising

Estimating CTR for New Ads in Search Advertising

Practical Experiences for Ad Click Prediction

A Simple Framework for Predicting CTR in Display Advertising

Data Science

AB Testing (Udacity) Learning Notes (3)

AB Testing (Udacity) Learning Notes (2)

AB Testing (Udacity) Learning Notes (1)

Data Visualization

Data Visualization with ggplot2 (1)

Decision Tree

Practical Comparison of XGBoost and LightGBM

LightGBM - A more Efficient GBM Framework

XGBoost - How it works

Machine Learning Overview Series (5) - Bagging and Random Forests

Machine Learning Overview Series (4) - Classification Tree

Machine Learning Overview Series (3) - Regression Tree

Deep Learning

Summary on Attention in NLP

FastText - Learning Sub-word Embeddings

GloVe - Word Embeddings Utilizing Global statistics

DeepFM - Combining FM and Neural Nets

Wide and Deep Learning for Recommender Systems

Denoising Autoencoder - A Robust Feature Extractor

Machine Learning Overview Series (7) - LSTM and GRU

Deep Learning for Anomaly Detection

Efficient Way to Train Word Embeddings

Models to Learn Word Representation

Neural Network on Language Model

Deep Learning Basis & Cheatsheet

Elasticsearch

Blog Analysis based on Elasticsearch

Ensemble Algorithms

Practical Comparison of XGBoost and LightGBM

LightGBM - A more Efficient GBM Framework

XGBoost - How it works

Machine Learning Overview Series (5) - Bagging and Random Forests

Factorization Models

DeepFM - Combining FM and Neural Nets

FFM - More Elaborated and Refined Factorization Machines

Factorization Machine - A General Predictor for Sparse Data

Matrix Factorization for Recommender Systems

GBDT

Practical Comparison of XGBoost and LightGBM

LightGBM - A more Efficient GBM Framework

XGBoost - How it works

Kaggle

Kaggle Milestone - Expert

First Journey through Kaggle

Language Model

Neural Network on Language Model

Stanford NLP (coursera) Notes (4) - Language Model

Linear Algebra

Linear Algebra Review

Linear Regression

Machine Learning Overview Series (2) - Linear Regression

Logistic Regression

Machine Learning Overview Series (1) - Logistic Regression

Machine Learning

Machine Learning Overview Series (8) - Kmeans

The Beauty of Mathematics in Computer Science Notes

DeepFM - Combining FM and Neural Nets

Estimating CTR for New Ads in Search Advertising

Practical Experiences for Ad Click Prediction

A Simple Framework for Predicting CTR in Display Advertising

Wide and Deep Learning for Recommender Systems

FFM - More Elaborated and Refined Factorization Machines

Factorization Machine - A General Predictor for Sparse Data

Matrix Factorization for Recommender Systems

L1 Norm Regularization Explained

Practical Comparison of XGBoost and LightGBM

LightGBM - A more Efficient GBM Framework

XGBoost - How it works

NBSVM - A Strong Classification Baseline

Stanford NLP (coursera) Notes (8) - Max Entropy Model

Stanford NLP (coursera) Notes (7) - Sentiment Analysis

Stanford NLP (coursera) Notes (6) - Text Classification and Naive Bayes

Kaggle Milestone - Expert

Bias and Variance Trade-Off

Machine Learning Overview Series (6) - Support Vector Machines

Machine Learning Overview Series (5) - Bagging and Random Forests

Machine Learning Overview Series (4) - Classification Tree

Machine Learning Overview Series (3) - Regression Tree

Machine Learning Overview Series (2) - Linear Regression

Machine Learning Overview Series (1) - Logistic Regression

First Journey through Kaggle

Practical Application of Metrics in Binary Classifications

Common Metrics in Binary Classification Problems

Mathematics

Simulation Sampling Algorithms

The Beauty of Mathematics in Computer Science Notes

Probability Review (2)

Probability Review (1)

Statistical Inference Review

Linear Algebra Review

L1 Norm Regularization Explained

Multivariate Transformation

Maximum Entropy Model

Stanford NLP (coursera) Notes (8) - Max Entropy Model

Metrics

Time Series Precision Recall

Practical Application of Metrics in Binary Classifications

Common Metrics in Binary Classification Problems

Model Evaluation

Time Series Precision Recall

Bias and Variance Trade-Off

Practical Application of Metrics in Binary Classifications

Common Metrics in Binary Classification Problems

NLP

Summary on Attention in NLP

FastText - Learning Sub-word Embeddings

GloVe - Word Embeddings Utilizing Global statistics

The Beauty of Mathematics in Computer Science Notes

Stanfold NLP (Coursera) Notes (20) - Text Summarization

Stanford NLP (Coursera) Notes (19) - Question Answering

Stanford NLP (Coursera) Notes (18) - Word Meaning and Similarity

Stanford NLP (Coursera) Notes (17) - Introduction to Ranked Retrieval

Stanford NLP (Coursera) Notes (16) - Introduction to Information Retrieval

Stanford NLP (Coursera) Notes (15) - Dependency Parsing

Stanford NLP (Coursera) Notes (14) - Lexicalized Parsing

Stanford NLP (Coursera) Notes (13) - Probabilistic Parsing

Stanford NLP (Coursera) Notes (12) - Parsing Introduction

Stanford NLP (coursera) Notes (11) - POS Tagging

Efficient Way to Train Word Embeddings

Models to Learn Word Representation

Neural Network on Language Model

NBSVM - A Strong Classification Baseline

Stanford NLP (coursera) Notes (10) - Relation Extraction

Stanford NLP (coursera) Notes (9) - Information Extraction and NER

Stanford NLP (coursera) Notes (8) - Max Entropy Model

Stanford NLP (coursera) Notes (7) - Sentiment Analysis

Stanford NLP (coursera) Notes (6) - Text Classification and Naive Bayes

Stanford NLP (coursera) Notes (5) - Spell Correction

Stanford NLP (coursera) Notes (4) - Language Model

Stanford NLP (coursera) Notes (3) - Edit Distance

Stanford NLP (coursera) Notes (2) - Text Processing

Stanford NLP (coursera) Notes (1) - Introduction

Naive Bayes

NBSVM - A Strong Classification Baseline

Stanford NLP (coursera) Notes (6) - Text Classification and Naive Bayes

Probability

Probability Review (2)

Probability Review (1)

Multivariate Transformation

Programming

Assignment operator in R

R

Assignment operator in R

Data Visualization with ggplot2 (1)

RNN

Machine Learning Overview Series (7) - LSTM and GRU

Recommender Sys

Recommender Systems Overview - From A Survey

Wide and Deep Learning for Recommender Systems

FFM - More Elaborated and Refined Factorization Machines

Factorization Machine - A General Predictor for Sparse Data

Matrix Factorization for Recommender Systems

SVM

NBSVM - A Strong Classification Baseline

Machine Learning Overview Series (6) - Support Vector Machines

Software Development

Blog Analysis based on Elasticsearch

Bash and Git Quick Reference

Statistics

Simulation Sampling Algorithms

Statistical Inference Review

Time Series

Time Series Precision Recall

Time Series Models Overview (2) - State Space Model and Kalman Filter

Time Series Models Overview (1) - ARIMA

Unsupervised Learning

Machine Learning Overview Series (8) - Kmeans

Denoising Autoencoder - A Robust Feature Extractor

Word Embeddings

FastText - Learning Sub-word Embeddings

GloVe - Word Embeddings Utilizing Global statistics

Efficient Way to Train Word Embeddings

Models to Learn Word Representation