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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…

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 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 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…

Now that we have implemented neural networks in pure Python, let’s move on to the preferred implementation method — using a dedicated (highly optimized) neural network library such as Keras. Today, I will discuss how to implement feedforward, multi-layer networks…

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…

First introduced by Rosenblatt in 1958, The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain is arguably the oldest and most simple of the ANN algorithms. Following this publication, Perceptron-based techniques were all the rage in…

We’ll study the fundamentals of neural networks in depth. We’ll start with a discussion of artificial neural networks and how they are inspired by the real-life biological neural networks in our own bodies. From there, we’ll review the classic Perceptron…