Rahul  Hickle

Rahul Hickle

1591351200

Learning FASTAI: Crack Detection in Concrete Structure

The power of transfer learning with FASTAI: Crack Detection in Concrete Structure
This blog post presents a practical application of computer vision classification. We will learn how to use the fascinating fastai library to do transfer learning for identifying cracks (damages) in concrete structures.

#transfer-learning #deep-learning #machine-learning #programming

What is GEEK

Buddha Community

Learning  FASTAI: Crack Detection in Concrete Structure
Rahul  Hickle

Rahul Hickle

1591351200

Learning FASTAI: Crack Detection in Concrete Structure

The power of transfer learning with FASTAI: Crack Detection in Concrete Structure
This blog post presents a practical application of computer vision classification. We will learn how to use the fascinating fastai library to do transfer learning for identifying cracks (damages) in concrete structures.

#transfer-learning #deep-learning #machine-learning #programming

Michael  Hamill

Michael Hamill

1618310820

These Tips Will Help You Step Up Anomaly Detection Using ML

In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two.

A common need when you analyzing real-world data-sets is determining which data point stand out as being different from all other data points. Such data points are known as anomalies, and the goal of anomaly detection (also known as outlier detection) is to determine all such data points in a data-driven fashion. Anomalies can be caused by errors in the data but sometimes are indicative of a new, previously unknown, underlying process.

#machine-learning #machine-learning-algorithms #anomaly-detection #detecting-data-anomalies #data-anomalies #machine-learning-use-cases #artificial-intelligence #fraud-detection

Ismael  Stark

Ismael Stark

1618128600

Credit Card Fraud Detection via Machine Learning: A Case Study

This is the second and last part of my series which focuses on Anomaly Detection using Machine Learning. If you haven’t already, I recommend you read my first article here which will introduce you to Anomaly Detection and its applications in the business world.

In this article, I will take you through a case study focus on Credit Card Fraud Detection. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. So the main task is to identify fraudulent credit card transactions by using Machine learning. We are going to use a Python library called PyOD which is specifically developed for anomaly detection purposes.

#machine-learning #anomaly-detection #data-anomalies #detecting-data-anomalies #fraud-detection #fraud-detector #data-science #machine-learning-tutorials

Jerad  Bailey

Jerad Bailey

1598891580

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Curated list of Outlier Detection Resources

In data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. — Wikipedia.

This post contents:

  1. Books
  2. Research Papers

Books

Outlier Analysis by Charu C. Aggarwal.

Outlier Ensembles by Charu C. Aggarwal, Saket Sathe.


Research Papers

Survey Papers

Anomaly detection: A survey

Novelty detection: a review — part 1: statistical approaches

Novelty detection: a review — part 2:: neural network based approaches

A Survey of Outlier Detection Methodologies

State-of-the-art Methods

LOF: Identifying Density-Based Local Outliers

Isolation Forest

Density Based Outlier Detection Methods

LOF: Identifying Density-Based Local Outliers

LOCI: fast outlier detection using the local correlation integral

Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection

OPTICS-OF: Identifying Local Outliers

A local density-based approach for outlier detection

An efficient algorithm for distributed density-based outlier detection on big data

LoOP: Local Outlier Probabilities

Distance Based Outlier Detection Methods

A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data

Distance-based Outlier Detection in Data Streams

Rapid Distance-Based Outlier Detection via Sampling

Angle-based outlier detection in high-dimensional data

Scalable distance-based outlier detection over high-volume data streams

Fast mining of distance-based outliers in high-dimensional datasets

Distance-Based Outlier Detection on Uncertain Data

Distance-based outlier detection: consolidation and renewed bearing

Clustering Based Outlier Detection Methods

Cluster-based outlier detection

Clustering-Based Outlier Detection Method

Efficient Clustering-Based Outlier Detection Algorithm for Dynamic Data Stream

An Outlier Detection Method Based on Clustering

Cluster Based Outlier Detection Algorithm for Healthcare Data

A Minimum Spanning Tree-Inspired Clustering-Based Outlier Detection Technique

Framework of Clustering-Based Outlier Detection

#anomaly-detection #outlier-detection #learning #resources #machine-learning #deep learning