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 models. Time to up your game! Now ...
Generative and Discriminative Models
Most of the Machine Learning and Deep Learning problems that you solve are conceptualized from the Generative and Discriminative Models. In Machine Learning, one can clearly distinguish between the ...
Experiment Logging with TensorBoard and wandb
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 ...
Image Matting with state-of-the-art Method “F, B, Alpha Matting”
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 objects on images from the ...
Bag of Tricks for Image Classification
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", etc. ) that ...
TensorBoard with PyTorch Lightning
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 of random chaos, ...
