Graph-based anomaly detection identifies unusual data points by analyzing graph structures, where nodes represent data points and edges depict relationships, such as distances or similarities. In this tutorial, I explain how to detect anomalies using a graph-based method in Python. The tutorial covers:
In this tutorial, we'll learn about DenseNet model and how to use a pre-trained DenseNet121 model for image classification with PyTorch. We'll go through the steps of loading a pre-trained model, preprocessing image, and using the model to predict its class label, as well as displaying the results.The tutorial covers:
In this tutorial, we'll learn about Inception model and how to use a pre-trained Inception-v3 model for image classification with PyTorch. We'll go through the steps of loading a pre-trained model, preprocessing image, and using the model to predict its class label, as well as displaying the results.The tutorial covers:
Feature selection, a technique in feature engineering, plays a key role in building effective machine learning models. Lasso regression, short for Least Absolute Shrinkage and Selection Operator, is a useful tool for selecting important features. It helps reduce model complexity, prevent overfitting, and makes the model easier to understand.
In this tutorial, we'll go through the steps for using Lasso regression to perform feature selection. This tutorial
will cover:
Let's get started.
In this tutorial, we'll learn about ResNet model and how to use a pre-trained ResNet-50 model for image classification with PyTorch. We'll go through the steps of loading a pre-trained model, preprocessing image, and using the model to predict its class label, as well as displaying the results.The tutorial covers:
