Archive | Machine Learning

Intro to anomaly detection with OpenCV, Computer Vision, and scikit-learn

In this tutorial, you will learn how to perform anomaly/novelty detection in image datasets using OpenCV, Computer Vision, and the scikit-learn machine learning library. Imagine this — you’re fresh out of college with a degree in Computer Science. You focused your studies specifically on computer vision and machine learning. Your first job out of school […]

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Detecting Parkinson’s Disease with OpenCV, Computer Vision, and the Spiral/Wave Test

In this tutorial, you will learn how to use OpenCV and machine learning to automatically detect Parkinson’s disease in hand-drawn images of spirals and waves. Today’s tutorial is inspired from PyImageSearch reader, Joao Paulo Folador, a PhD student from Brazil. Joao is interested in utilizing computer vision and machine learning to automatically detect and predict […]

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Machine Learning in Python

Struggling to get started with machine learning using Python? In this step-by-step, hands-on tutorial you will learn how to perform machine learning using Python on numerical data and image data. By the time you are finished reading this post, you will be able to get your start in machine learning. To launch your machine learning […]

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ImageNet: VGGNet, ResNet, Inception, and Xception with Keras

A few months ago I wrote a tutorial on how to classify images using Convolutional Neural Networks (specifically, VGG16) pre-trained on the ImageNet dataset with Python and the Keras deep learning library. The pre-trained networks inside of Keras are capable of recognizing 1,000 different object categories, similar to objects we encounter in our day-to-day lives with high […]

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Stochastic Gradient Descent (SGD) with Python

In last week’s blog post, we discussed gradient descent, a first-order optimization algorithm that can be used to learn a set of classifier coefficients for parameterized learning. However, the “vanilla” implementation of gradient descent can be prohibitively slow to run on large datasets — in fact, it can even be considered computationally wasteful. Instead, we should apply Stochastic […]

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