The LeNet architecture is a seminal work in the deep learning community, first introduced by LeCun et al. in their 1998 paper, Gradient-Based Learning Applied to Document Recognition. As the name of the paper suggests, the authors’ motivation behind implementing…

In our previous tutorial, we discussed LeNet, a seminal Convolutional Neural Network in the deep learning and computer vision literature. VGGNet, (sometimes referred to as simply VGG), was first introduced by Simonyan and Zisserman in their 2014 paper, Very Deep…

One concept we have not discussed yet is architecture visualization, the process of constructing a graph of nodes and associated connections in a network and saving the graph to disk as an image (i.e., PNG, JPG, etc.). Nodes in the…

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 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…

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…