AI, Deep Learning & Algorithms

Deep Learning

• Variational inference
• Difference between AutoEncoder (AE) and Variational AutoEncoder (VAE) | by Aqeel Anwar | Towards Data Science
• CS231n Convolutional Neural Networks for Visual Recognition
• Deep Learning
• Open source guides/codes for mastering deep learning to deploying deep learning
• Using LSTM in PyTorch: A Tutorial With Examples – Weights & Biases
• What are Diffusion Models? | Lil'Log

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Large Language Models (LLM)

• LLaVA
• AI image generators often give racist and sexist results: can they be fixed?
• Generating images with DDPMs: A PyTorch Implementation | by Brian Pulfer | Medium
• Trying to Understand Stable Diffusion | by Eric Silberstein | Klaviyo Engineering
• Environment variables
• Pipelines
• Democratising Knowledge Graphs — BioCypher
• BioChatter Light
• Exbert
• Rich Sutton's Home Page
• The Bitter Lesson
• Vision Transformers (ViT) Explained | Pinecone
• ctheodoris/Geneformer · Hugging Face
• What's new | Updates on my research and expository papers, discussion of open problems, and other maths-related topics. By Terence Tao
• Testing Microscope Image Compatibility with Space Ranger - 10x Genomics

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Machine Learning & Algorithms

• How can we quantify similarity between time series? | by Alexander Bader | Gorilla Tech Blog
• Understanding how kallisto works – IRIC's Bioinformatics Platform
• The Unreasonable Effectiveness of Recurrent Neural Networks
• Illustrated Guide to LSTM’s and GRU’s: A step by step explanation | by Michael Phi | Towards Data Science
• Comparing Ensembl GTF and cDNA | R-bloggers
• List of Awesome Open Source Machine Learning Project Repos | by Ravindu Senaratne | Towards Data Science
• Peak signal detection
• Section 4 (Week 4)
• Overfitting in Machine Learning: What It Is and How to Prevent It
• The 5 Levels of Machine Learning Iteration
• 13: Bias/Variance and Model Selection
• OmicsML/awesome-deep-learning-single-cell-papers
• Rethinking Attention with Performers – Google Research Blog
• 9.5 Shapley Values | Interpretable Machine Learning
• Using Random Survival Forests — scikit-survival 0.22.2

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Dimension Reduction

• PCA
• UMAP

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