deep learning
Our last post on the MRNet challenge presented a simple way to approach it. There you learned to make a separate model for each disease. And ended up with three
In this post, we will learn about Video Classification. We will go over a number of approaches to make a video classifier for Human Activity Recognition. Basically, you will learn
Can we distinguish one person from another by looking at the face? We can probably list several features such as eye color, hairstyle, skin tone, the shape of the nose
When approaching a problem using Machine Learning or Deep Learning, researchers often face a necessity of model tuning because the chosen method usually depends on various hyperparameters and used data.
The foreground is the part of a view or picture, that is nearest to you when you look at it (Oxford dictionary). We, humans, are usually good at distinguishing foreground
Introduction Image classification is a key task in Computer Vision. In an image classification task, the input is an image, and the output is a class label (e.g. “cat”, “dog”,
In this post, we continue to consider how to speed up inference quickly and painlessly if we already have a trained model in PyTorch. In the previous post We discussed
A picture is worth a thousand words! As computer vision and machine learning experts, we could not agree more. Human intuition is the most powerful way of making sense out
Neural network usage usually takes a lot of computations, but in our modern world, even a smartphone can be a device to run your trained neural model. Today we will
Let’s play rock, paper scissors. You think of your move and I’ll make mine below this line in 1…2…and 3. I choose ROCK. Well? …who won. It doesn’t matter cause
In our recent post about receptive field computation, we examined the concept of receptive fields using PyTorch. We learned receptive field is the proper tool to understand what the network
The life of a machine learning engineer consists of long stretches of frustration and a few moments of joy! First, struggle to get your model to produce good results on
