Greetings. This course is not intended for beginners and it is more piratically oriented. Though I tried my best to explain why I performed a particular step but as said I put little to no effort into explaining what is Convolution neural networks, how the optimizer works, how ResNet, DenseNet model was created, etc.

My focus was mainly on how to participate in a competition, how to get data and train a model on that data, and how to make a submission.

The course covers the following topics

1. Binary Classification

   1. Get the data

   2. Read data

   3. Apply augmentation

   4. How data flows from folders to GPU

   5. Train a model

   6. Get accuracy metric and loss

2. Multi class classification (CXR covid19 competition)

   1. Albumentations augmentations

   2. Write a custom data loader

   3. Use publicly pre trained model on XRay

   4. Use learning rate scheduler

   5. Use different callback functions

   6. Do 5 fold cross validations when images are in the folder

   7. Train, save, and load model

   8. Get test predictions via ensemble learning

   9. Submit predictions to the competition page

3. Multi label classification (ODIR competition)

   1. Apply augmentation on two images simultaneously

   2. Make a parallel network to take two images simultaneously

   3. Modify binary cross entropy loss to focal loss

   4. Use custom metric provided by competition organizer to get an evaluation

   5. Get predictions of the test set

4. Capstone Project (Covid 19 Infection Percentage Estimation)

   1. How to come up with a solution

   2. Code walkthrough

   3. The secret sauce of model ensemble