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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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Programming for Biology

Python for Biologists
R for Biologists
Perl for Biologists
MATLAB for Biologists
SAS for Biologists
BioJava for Biologists

Data & Databases

SQL for Biological Databases
Bioinformatics Databases
Big Data Mining for Biologists

AI & Machine Learning

Machine Learning for Biologists
Artificial Intelligence for Biologists
MLOps for Biologists
AI Tools for the Bioinformatics Lab

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Machine Learning for Biologists82 chapters

Chapter 7.1 — From Perceptron to Artificial Neural Networks (ANN)
Chapter 7.2 — Convolutional Neural Networks (CNNs) for Biological Images
Chapter 7.3 — Recurrent Networks: RNN, LSTM, and GRU for Biological Sequences
Chapter 7.4 — Transformers and Attention
Chapter 7.5 — Autoencoders and Variational Autoencoders (VAEs)
Chapter 7.6 — Generative Adversarial Networks (GANs)

/Machine Learning for Biologists/PART 7 — Deep Learning

Chapter 33 of 826 min read

Chapter 7.5 — Autoencoders and Variational Autoencoders (VAEs)

1. Learning Objectives Understand autoencoders as unsupervised compression/reconstruction networks Understand how VAEs extend autoencoders into generative probabilistic models Apply both to biological dimensionality…

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PreviousChapter 7.4 — Transformers and Attention NextChapter 7.6 — Generative Adversarial Networks (GANs)