Chris Kuo/Dr. Dataman

Pinned

Published in
Dataman in AI

Handbook of Anomaly Detection: With Python Outlier Detection — (1) Introduction

Anomaly detection is the detection for any rare events that deviate significantly from the majority of the data. Those rare events, called…

Oct 9, 2022

Handbook of Anomaly Detection: With Python Outlier Detection — (1) Introduction

Oct 9, 2022

Pinned

Published in
Dataman in AI

Explain Your Model with the SHAP Values

Use the SHAP Values to Explain Any Complex ML Model

Sep 14, 2019

Sep 14, 2019

Pinned

Published in
Dataman in AI

Transfer Learning for Image Classification — (2) Pre-trained Image Models

Hands on with Pre-trained models

Jan 30, 2023

Transfer Learning for Image Classification — (2) Pre-trained Image Models

Jan 30, 2023

Pinned

Published in
Dataman in AI

The SHAP Values with H2O Models

Many machine learning algorithms are complicated and not easy to understand, even though they have rendered an impressive level of…

Nov 24, 2021

Nov 24, 2021

Pinned

Published in
Dataman in AI

Data Science Q&A — (14) Sampling techniques for imbalanced data

Q1. What is class imbalance in supervised learning?

Aug 24

Data Science Q&A — (14) Sampling techniques for imbalanced data

Aug 24

Published in
Dataman in AI

Data science Q&A — (13) Regularization

Q1. What is overfitting in machine learning, and why is it a problem?

Aug 24

Aug 24

Published in
Dataman in AI

Data science Q&A — (12) Supervised learning primer

Q1. What is supervised learning, and can you name some common algorithms?

Aug 24

Data science Q&A — (12) Supervised learning primer

Aug 24

Published in
Dataman in AI

Data Science Q&A — (11) Autoencoders

Q1. What inspired the development of neural networks in artificial intelligence?

Aug 24

Aug 24

Published in
Dataman in AI

Data science Q&A — (15) Representation Learning for Outlier Detection

Q1: What is the primary goal of feature engineering in anomaly detection in response to evolving anomalous patterns?

Aug 24

Data science Q&A — (15) Representation Learning for Outlier Detection

Aug 24

Handbook of Anomaly Detection: With Python Outlier Detection — (1) Introduction

Anomaly detection is the detection for any rare events that deviate significantly from the majority of the data. Those rare events, called…

Explain Your Model with the SHAP Values

Use the SHAP Values to Explain Any Complex ML Model

Transfer Learning for Image Classification — (2) Pre-trained Image Models

Hands on with Pre-trained models

The SHAP Values with H2O Models

Many machine learning algorithms are complicated and not easy to understand, even though they have rendered an impressive level of…

Top Data Science Interview Questions and Answers

You receive a data science interview opportunity from your dream company. You have surveyed many the-top-50-question types of articles but…

Data Science Q&A — (14) Sampling techniques for imbalanced data

Q1. What is class imbalance in supervised learning?

Data science Q&A — (13) Regularization

Q1. What is overfitting in machine learning, and why is it a problem?

Data science Q&A — (12) Supervised learning primer

Q1. What is supervised learning, and can you name some common algorithms?

Data Science Q&A — (11) Autoencoders

Q1. What inspired the development of neural networks in artificial intelligence?

Data science Q&A — (15) Representation Learning for Outlier Detection

Q1: What is the primary goal of feature engineering in anomaly detection in response to evolving anomalous patterns?

Chris Kuo/Dr. Dataman