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The full list of tutorials can be found at. This notebook is part of a lecture series on Deep Learning at the University of Amsterdam. As the hype of the TransformerĪrchitecture seems not to come to an end in the next years, it is important to understand how it works, and have implemented it yourself, which we will do in this notebook. Transformers with an incredible amount of parameters can generate long, convincing essays, and opened up new application fields of AI. Since the paper Attention Is All You Need by Vaswani et al. had been published in 2017, the Transformer architecture has continued to beat benchmarks in many domains, most importantly in Natural Language Processing. In this tutorial, we will discuss one of the most impactful architectures of the last 2 years: the Transformer model. Tutorial 5: Transformers and Multi-Head Attention ¶

Multi-agent Reinforcement Learning With WarpDrive.

Finetune Transformers Models with PyTorch Lightning.

PyTorch Lightning CIFAR10 ~94% Baseline Tutorial.

GPU and batched data augmentation with Kornia and PyTorch-Lightning.

Tutorial 13: Self-Supervised Contrastive Learning with SimCLR.

Tutorial 12: Meta-Learning - Learning to Learn.

Tutorial 10: Autoregressive Image Modeling.

Tutorial 9: Normalizing Flows for Image Modeling.

Tutorial 7: Deep Energy-Based Generative Models.

Tutorial 6: Basics of Graph Neural Networks.

Tutorial 5: Transformers and Multi-Head Attention.

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