Deep learning with PyTorch | Medical Imaging Competitions

This course is outdated because it is based on PyTorch lightning and alot of thing has been changed since the release of this course. Further some of datasets in this course are no more available for public anymore. So I am not providing support for this course. I want to make this course free, but udemy is not allowing to do so because of content length. The reason why I am not archiving this course, because its still relevant if you want to gain concept of medical imaging competition. Greetings. This course is not intended for beginners, and it is more practically oriented. Though I tried my best to explain why I performed a particular step, I put little to no effort into explaining basic concepts such as Convolution neural networks, how the optimizer works, how Res Net, Dense Net model was created etc. This course is for those who have worked on CIFAR, MNIST data and want to work in real-life scenarios My focus was mainly on how to participate in a competition, get data and train a model on that data, and make a submission. In this course PyTorch lightning is used The course covers the following topics Binary Classification Get the data Read data Apply augmentation How data flows from folders to GPU Train a model Get accuracy metric and loss Multi-class classification (CXR-covid19 competition)Albumentations augmentations Write a custom data loader Use publicly pre-trained model on XRay Use learning rate scheduler Use different callback functions Do five fold cross-validations when images are in a folder Train, save and load model Get test predictions via ensemble learning Submit predictions to the competition page Multi-label classification (ODIR competition)