DL. Home

	slides	colab	html	markdown	notebook
Lecture 1: Basic concepts	.html	link	.html	.md	.ipynb
Lecture 2: Convolutional neural networks	.html	link	.html	.md	.ipynb
Lecture 3: Training neural networks (better)	.html	link	.html	.md	.ipynb
Lecture 4: СV tasks	.html	link	.html	.md	.ipynb
Lecture 5: Modelling sequences	.html	link	.html	.md	.ipynb
Lecture 6: Vision Transformers	.html	link	.html	.md	.ipynb
Lecture 7: Graph Neural Networks	.html	link	.html	.md	.ipynb
Lecture 8: General tricks for efficient training	.html	link	.html	.md	.ipynb
Lecture 9: Training large models	.html	link	.html	.md	.ipynb
Lecture 10: Contrastive learning / self-supervised learning	.html	link	.html	.md	.ipynb
Lecture 11: One-shot/Zero-shot/Few-shot learning	.html	link	.html	.md	.ipynb
Lecture 12: Adversarial attacks and training	.html	link	.html	.md	.ipynb
Lecture 13: Generative models I	.html	link	.html	.md	.ipynb
Lecture 14: Generative models II (Generative adversarial models)	.html	link	.html	.md	.ipynb
Lecture 15: Generative models III (Score-based and diffusion models)	.html	link	.html	.md	.ipynb
Lecture 16: ICLR conference 2013 highlights	.html	link	.html	.md	.ipynb

	colab	html	markdown	notebook
PyTorch Introduction	link	.html	.md	.ipynb
Experiment tracking (logging) in PyTorch.	link	.html	.md	.ipynb
Regularization tricks for NN	link	.html	.md	.ipynb
Object detection	link	.html	.md	.ipynb
Semantic Segmentation (solutions)	link	.html	.md	.ipynb
Semantic Segmentation	link	.html	.md	.ipynb
Graph Neural Networks (GNN) and how to use them	link	.html	.md	.ipynb
ViT from scratch	link	.html	.md	.ipynb
Tricks to train big models	link	.html	.md	.ipynb
define laten variable dimensionality	link	.html	.md	.ipynb
def run():	link	.html	.md	.ipynb

Lectures