The field of Computer Vision has for years been dominated by Convolutional Neural Networks (CNNs). Through the use of filters, these networks are able to generate simplified versions of the input image by creating feature maps that highlight the most relevant parts. These features are then used by a multi-layer perceptron to perform the desired classification. But recently … [Read more...] about Vision Transformers or Convolutional Neural Networks? Both!
Authors: Duncan Wang, Arnaud Guzman-Annès, Sophie Courtemanche-Martel & James Hogan The recycling problem Reduce, Reuse, and Recycle. Otherwise known as the three Rs of waste management, this aphorism has been largely popularized as the solution for solving the rising waste crisis. In North America, rising environmental awareness amongst the general public and the … [Read more...] about How AI Can Help Us Recycle
Over the years, variants of CNN architectures have been developed, leading to amazing advances in the field of deep learning. A good measure of this progress is the error rates in competitions such as the ILSVRC ImageNet challenge. In this competition, the top-5 error rate for image classification fell from over 26% to less than 3%. In this article, we will look at some of the … [Read more...] about 4 CNN Networks Every Machine Learning Engineer Should Know
In this post, we’re going to do a deep-dive on something most introductions to Convolutional Neural Networks (CNNs) lack: how to train a CNN, including deriving gradients, implementing backprop from scratch (using only numpy), and ultimately building a full training pipeline! This post assumes a basic knowledge of CNNs. My introduction to … [Read more...] about CNNs, Part 2: Training a Convolutional Neural Network
There’s been a lot of buzz about Convolution Neural Networks (CNNs) in the past few years, especially because of how they’ve revolutionized the field of Computer Vision. In this post, we’ll build on a basic background knowledge of neural networks and explore what CNNs are, understand how they work, and build a real one from scratch (using only numpy) in … [Read more...] about CNNs, Part 1: An Introduction to Convolutional Neural Networks
If We Want Machines to Think, We Need to Teach Them to See — Fei-Fei Li Throughout this article, I will discuss some of the more complex aspects of convolutional neural networks and how they related to specific tasks such as object detection and facial recognition. The topics that will be discussed in this tutorial are: CNN reviewReceptive Fields and Dilated … [Read more...] about Advanced Topics in Deep Convolutional Neural Networks
The advent of powerful and versatile deep learning frameworks in recent years has made it possible to implement convolution layers into a deep learning model an extremely simple task, often achievable in a single line of code. However, understanding convolutions, especially for the first time can often feel a bit unnerving, with terms like kernels, filters, channels and so … [Read more...] about Intuitively Understanding Convolutions for Deep Learning