dimensionality reduction – Andrew Fairless, Ph.D.

What I Read: Use-cases, inverted PCA

By Andrew Fairless on October 16, 2024July 14, 2024

https://www.youtube.com/watch?v=ZK3_T-UgKWM Use-cases for inverted PCA:probabl.Jul 11, 2024 “A lot of people know of PCA for it’s ability to reduce the dimensionality of a dataset. It can turn a wide datasetContinue readingWhat I Read: Use-cases, inverted PCA

What I Read: LoRA from Scratch

By Andrew Fairless on April 4, 2024March 1, 2024

https://lightning.ai/lightning-ai/studios/code-lora-from-scratch Code LoRA from ScratchSebastian Raschka “LoRA, which stands for Low-Rank Adaptation, is a popular technique to finetune LLMs more efficiently,,,. This Studio explains how LoRA works by coding itContinue readingWhat I Read: LoRA from Scratch

What I Read: Deep learning, single-cell sequencing

By Andrew Fairless on March 6, 2024January 25, 2024

https://thegradient.pub/deep-learning-for-single-cell-sequencing-a-microscope-to-uncover-the-rich-diversity-of-individual-cells/ Deep learning for single-cell sequencing: a microscope to see the diversity of cellsFatima Zahra El Hajji1/13/24 1:12 PM “…we will explore the pivotal role that Deep Learning, in particular,Continue readingWhat I Read: Deep learning, single-cell sequencing

What I Read: Unsupervised Learning Metrics

By Andrew Fairless on June 8, 2023May 1, 2023

https://www.kdnuggets.com/2023/04/exploring-unsupervised-learning-metrics.html Exploring Unsupervised Learning MetricsCornellius Yudha WijayaApril 13, 2023 “This article will discuss the metrics used to evaluate unsupervised machine learning algorithms…”

What I Read: Concept Drift Without Labeled Data

By Andrew Fairless on September 26, 2022August 22, 2022

https://concept-drift.fastforwardlabs.com/ Inferring Concept Drift Without Labeled DataFF22Aug 2021 “…we explore broadly applicable approaches for dealing with concept drift when labeled data is not readily accessible. We’ll start by defining whatContinue readingWhat I Read: Concept Drift Without Labeled Data

What I Read: Game theory for large-scale data analysis

By Andrew Fairless on June 7, 2021May 14, 2021

https://deepmind.com/blog/article/EigenGame 06 May 2021Game theory as an engine for large-scale data analysisBrian McWilliamsIan GempClaire Vernade “…we reformulated principal component analysis (PCA)… as a competitive multi-agent game we call EigenGame. PCAContinue readingWhat I Read: Game theory for large-scale data analysis

What I Read: Diffusion Map for manifold learning

By Andrew Fairless on December 19, 2020December 8, 2020

https://randomwalk.in/python/ml/2020/03/14/Diffusion-Map.html Diffusion Map for manifold learning, Theory and Implementation “Manifold learning is an approach to non-linear dimensionality reduction…. In this blog, we learn one of the many techniques in manifoldContinue readingWhat I Read: Diffusion Map for manifold learning

What I Read: Neighbourhood Components Analysis

By Andrew Fairless on December 13, 2020December 1, 2020

https://kevinzakka.github.io/2020/02/10/nca/ Kevin’s BlogkNN classification using Neighbourhood Components AnalysisFeb 10, 2020 “Long story short, NCA allows you to learn a linear transformation of your data that maximizes k-nearest neighbours performance. ByContinue readingWhat I Read: Neighbourhood Components Analysis

SHAP Tutorial

By Andrew Fairless on August 25, 2020August 23, 2020

Several months ago, I wrote a blog post for my company describing the importance of model interpretability in healthcare settings. Below is a tutorial on how to use the SHAPContinue readingSHAP Tutorial

Tag: dimensionality reduction