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ComputingLifeSci

A complete computational biology curriculum — 23 courses covering programming, databases, machine learning, genomics and research infrastructure. Written for biologists, not computer scientists.

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Artificial Intelligence for Biologists
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Artificial Intelligence for Biologists45 chapters

Chapter 6.1 — Artificial Neurons and the Perceptron
Chapter 6.2 — Neural Networks: Feedforward Networks and Architecture
Chapter 6.3 — Convolutional Neural Networks (CNNs) for Biological Images
Chapter 6.4 — Sequence Models: RNN, LSTM, GRU, and Attention
Chapter 6.5 — Transformers: The Architecture Behind Modern AI
Chapter 6.6 — Autoencoders, GANs, and Diffusion Models

/Artificial Intelligence for Biologists/PART 6 — DEEP LEARNING

Chapter 21 of 454 min read

Chapter 6.5 — Transformers: The Architecture Behind Modern AI

Learning Objectives Understand the Transformer architecture at a conceptual level Understand self attention and why Transformers replaced RNNs for most tasks Connect Transformers to biology specific models (protein…

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PreviousChapter 6.4 — Sequence Models: RNN, LSTM, GRU, and Attention NextChapter 6.6 — Autoencoders, GANs, and Diffusion Models