Curso
Transformer Models with PyTorch
Avanzado
Actualizado 3/2025Comienza el curso gratis
Incluido conPremium or Teams
PyTorchArtificial Intelligence2 horas7 vídeos23 Ejercicios1,900 XPCertificado de logros
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Descripción del curso
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.Prerrequisitos
Deep Learning for Text with PyTorch1
The Building Blocks of Transformer Models
2
Building Transformer Architectures
Transformer Models with PyTorch
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