Article on building a Deep Learning Model that takes text and numerical inputs and returns Regression and Classification outputs.
One of the best ways to learn about convolutional neural networks (CNNs) is to write one from scratch! In this post we look to use PyTorch and the CIFAR-10 dataset to create a new neural network.
Image segmentation makes it easier to work with computer vision applications. Here we look at U-Net, a convolutional neural network designed for biomedical applications.
This blogpost is an in-depth discussion of the Google Brain paper titled "Searching for activation functions" which has since revived research into activation functions.
This article provides an in-depth look at the CVPR 2018 paper titled "xUnit: Learning a Spatial Activation Function for Efficient Image Restoration" and its importance in the domain of image reconstruction.
This article covers a deeper level understanding of Question Answering models in NLP, the datasets commonly used, and how to choose a pre-trained model by considering various factors like the document structure, runtime cost, etc.
