How to classify MNIST images with convolutional neural network

Introduction An artificial intelligence system called a convolutional neural network (CNN) has gained a lot of popularity recently. For jobs like image recognition, where we want to teach a computer to recognize things in a picture, they are especially well suited. CNNs operate by dissecting an image into increasingly minute components, or “features.” The network then examines each feature and searches for patterns shared by various objects. For instance, a CNN might come to understand that some pixel patterns are frequently linked to faces, while others are linked to vehicles or trees.

Deep learning to predict cancer from healthy controls using TCRseq data

Sign up for my newsletter to not miss a post like this The T-cell receptor (TCR) is a special molecule found on the surface of a type of immune cell called a T-cell. Think of T-cells like soldiers in your body’s defense system that help identify and attack foreign invaders like viruses and bacteria. The TCR is like a sensor or antenna that allows T-cells to recognize specific targets, kind of like how a key fits into a lock.

Basic tensor/array manipulations in R

Sign up for my newsletter to not miss a post like this In my last post, I showed you how to build a neural network in Keras with less than 20 lines of code. One of the key road blocks for beginners is to transform the input to the right shape of tensor (the deep learning terminology) or array (the R built-in type). In this post, I am going to show you some basic manipulations of the array.