In this article we will shed some light on Pooling in CNN. We will understand the importace of Pooling Layer. Additionally, a code demonstration for Image Classification utilizing the famous CIFAR-10 dataset is provided to enhance comprehension.
In this article, we examine the theoretical design behind the popular Transformers architecture, and attempt to explain the underlying mechanisms that have lead to its success in such a wide array of AI disciplines.
In this review, we examine popular text summarization models, and compare and contrast their capabilities for use in our own work.
In this article, we take a look at some of the fundamental concepts required for constructing neural networks from scratch. This includes detailed explanations of NN layers, activation functions, and loss functions.
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.
