In this article, we explore what dimensions imply in a convolutional neural network context. We show how to create a custom convnet as a baseline model, and then proceed to create new versions of it, with increased width, increased depth and the last one with both increased depth and width.
Batch normalization is a term commonly mentioned in the context of convolutional neural networks. In this article, we are going to explore what it actually entails and its effects, if any, on the performance or overall behavior of convolutional neural networks.
In this article, we explore the whys and the hows behind the fundamental process of pooling in CNN architectures, and then compare two common techniques: max and average pooling.
In this article, we explore what global average and max pooling entail. We discuss why they have come to be used and how they measure up against one another. We also developed an intuition into why they work by performing a biopsy of our convnets and visualizing intermediate layers.
In this blog post we take an in depth look at the Transformer model architecture, and demo its functionality by rebuilding the model from scratch in Python.
In this blog post we explore the differences between deed-forward and feedback neural networks, look at CNNs and RNNs, examine popular examples of Neural Network architectures, and their use cases.
In this article, we explore concepts related to convolutional neural network architectures with the intention of building our understanding enough to create and understand the capabilities of an AlexNet model, from scratch.
This blog details different techniques for filtering image data and explores what these filters actually do to an image as it passes through the layers of a Convolutional Neural Network (CNN). https://blog.paperspace.com/filters-in-convolutional-neural-networks/
In this article, we explore how and why we use padding in CNNs in computer vision tasks. We'll then jump into a full coding demo showing the utility of padding.
