Transformer Architecture: The X Gomega

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Pretraining Sequence-to-Sequence Models: BART and T5

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

From Attention to Transformers

Explores the evolution from attention mechanisms to transformers in modern NLP, emphasizing the significance of self-attention and cross-attention.

Transformers in Vision

Explores Transformers in computer vision, focusing on 'Attention Is All You Need' architecture and its applications in visual tasks.

Neural Machine Translation

Explores the evolution and challenges of Neural Machine Translation systems and the evaluation metrics used in this field.

Transformers in Vision

Explores the evolution of visual intelligence models, focusing on Transformers and their applications in computer vision and natural language processing.

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Machine Translation: Sequence-to-Sequence and Attention

Explores the advancements in Machine Translation, focusing on Sequence-to-Sequence models and Attention mechanisms.

Foundations of Deep Learning: Transformer Architecture Overview

Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.

Non conceptual knowledge systems

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Transformers: Overview and Self-Attention

Provides an overview of Transformers, self-attention, multi-headed attention, and the Transformer decoder and encoder.