Transformer Models with PyTorch

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Aktualisierte 05.2025

What makes LLMs tick? Discover how transformers revolutionized text modeling and kickstarted the generative AI boom.

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Kursbeschreibung

Deep-Dive into the Transformer Architecture

Transformer models have revolutionized text modeling, kickstarting the generative AI boom by enabling today's large language models (LLMs). In this course, you'll look at the key components in this architecture, including positional encoding, attention mechanisms, and feed-forward sublayers. You'll code these components in a modular way to build your own transformer step-by-step.

Implement Attention Mechanisms with PyTorch

The attention mechanism is a key development that helped formalize the transformer architecture. Self-attention allows transformers to better identify relationships between tokens, which improves the quality of generated text. Learn how to create a multi-head attention mechanism class that will form a key building block in your transformer models.

Build Your Own Transformer Models

Learn to build encoder-only, decoder-only, and encoder-decoder transformer models. Learn how to choose and code these different transformer architectures for different language tasks, including text classification and sentiment analysis, text generation and completion, and sequence-to-sequence translation.

Voraussetzungen

Deep Learning for Text with PyTorch

The Building Blocks of Transformer Models

Kapitel starten

Transformers with PyTorch

Kursbeschreibung

Deep-Dive into the Transformer Architecture

Implement Attention Mechanisms with PyTorch

Build Your Own Transformer Models

Leistungsnachweis verdienen

Mach mit .css-nklxlk{color:var(--wf-brand--main, #03EF62);}16 Millionen Lernende und starte Transformer Models with PyTorch heute!

Kostenloses Konto erstellen

Mach mit 16 Millionen Lernende und starte Transformer Models with PyTorch heute!