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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 29 of 825 min read

Chapter 7.1 — From Perceptron to Artificial Neural Networks (ANN)

1. Learning Objectives Understand the historical Perceptron and its limitations Consolidate the ANN foundation from Chapter 5.7 before moving to specialized architectures Understand why "deep" (multi layer) networks are…

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PreviousChapter 6.3 — Independent Component Analysis (ICA) and Association Rule Mining NextChapter 7.2 — Convolutional Neural Networks (CNNs) for Biological Images