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Transformer Models with PyTorch
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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.Prerequisites
Deep Learning for Text with PyTorchThe Building Blocks of Transformer Models
Building Transformer Architectures
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FAQs
What will I learn about in this course?
This course will teach you about the different components that make up the transformer architecture: positional encoding, attention mechanisms, and feed-forward sublayers. You'll use these components to build your own transformer models with PyTorch.
Who is this course intended for?
The course is aimed at prospective or practising ML, AI, or LLM Engineers who wish to understand how to build their own transformer models, rather than relying on pre-trained LLMs.
What are transformers and why are they important?
Transformer models are the defacto choice of deep learning architecture for modeling text data, surpassing more traditional RNNs and CNNs. The transformer architecture is used in popular LLMs like GPT-4, Meta's Llama, and Anthropic's Claude.
What are attention mechanisms?
Attention mechanisms allow transformer models to capture relationships between tokens, even if they are not positioned closely together in the sequence. This improves the overall quality of generated text.
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