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In this tutorial, you will learn how to tune machine learning model hyperparameters with scikit-learn and Python. This tutorial is part one in a four-part series on hyperparameter tuning: Introduction to hyperparameter tuning with scikit-learn and Python (today’s post) Grid…

A common question I get asked is: Are Convolutional Neural Networks invariant to changes in translation, rotation, and scaling? Is that why they are such powerful image classifiers? To answer this question, we first need to discriminate between the individual…

Our entire review of machine learning and neural networks thus far has been leading up to this point: understanding Convolutional Neural Networks (CNNs) and the role they play in deep learning. In traditional feedforward neural networks, each neuron in the…

CNN Building Blocks Neural networks accept an input image/feature vector (one input node for each entry) and transform it through a series of hidden layers, commonly using nonlinear activation functions. Each hidden layer is also made up of a set…

In this tutorial, you will learn how to perform edge detection using OpenCV and the Canny edge detector. Previously, we discussed image gradients and how they are one of the fundamental building blocks of computer vision and image processing. A…

In this tutorial, you will learn about image gradients and how to compute Sobel gradients and Scharr gradients using OpenCV’s cv2.Sobel function. Image gradients are a fundamental building block of many computer vision and image processing routines. We use gradients…

In this tutorial, you will learn about adaptive thresholding and how to apply adaptive thresholding using OpenCV and the cv2.adaptiveThreshold function. Last week, we learned how to apply both basic thresholding and Otsu thresholding using the cv2.threshold function. A diverse…

In this tutorial, you will learn how to implement face recognition using the Eigenfaces algorithm, OpenCV, and scikit-learn. Our previous tutorial introduced the concept of face recognition — detecting the presence of a face in an image/video and then subsequently…

Backpropagation is arguably the most important algorithm in neural network history — without (efficient) backpropagation, it would be impossible to train deep learning networks to the depths that we see today. Backpropagation can be considered the cornerstone of modern neural…