1. Course logistic. Machine Learning, Deep learning. Computer vision.
2. Image classification(what is nnets) + needfull tools for this course (python(pycharm), numpy, pytorch, tutorial for gcloud).
3. How to train: loss function and optimization, backpropogation.
4. How to train: data representation (image), batch normalization, dropout.
5. What is convolution + pytorch.
6. Pytorch (tensor, datasets, cuda, nets on pytorch, cuda) + pytorch tutorials.
7. Into kaggle + hardware.
8. The main architectures.
9. Visualizing and Understanding (Feature visualization and inversion Adversarial examples DeepDream and style transfer).
10. Transfer learning + tips and tricks (augmentation, ensembles + cross-validation, mixup, labelsmoothing + lr_schedulers).
11. Another comuter vision problems (segmentation, detection).
12. The most popular tasks today (face recognition, self-diving, deepfake, gans).
13. May be some overview of articles.
14. Optimization nnets (tensorrt, pruning, knowledge distillation).
Georgy Surin, computer vision / deep learning engineer at Inspector-cloud
3 silver medal, 1 bronze
7th place at Seismic challenge (https://boosters.pro)