#dimensionality-reduction

T-SNE

t-Distributed Stochastic Neighbour Embedding (t-SNE)

Seeing Beyond 3D: How t-SNE Turns Complex Data into Strategic Business Insight

In today’s data-intensive economy, organisations routinely manage high-dimensional data. Datasets are characterized by hundreds or even…

Visualizing Millions of Points in Seconds: A Deep Dive Into GPU Accelerated t-SNE

How tsne-cuda Turns a Notoriously Slow Dimensionality Reduction Technique Into a Near Instant Tool

Here I stay

The real source of abbur iske

The AI Scrap of Big Cities Is a Moneymaking Weapon in Small-Town China

If you have just dragged your suitcase from a high-rise office in Beijing, Shanghai, Guangzhou, or Shenzhen back to your small county…

Understanding PCA (Principal Component Analysis): The “Data Compression Without Losing Important…

Machine Learning models work with data.

PCA (Principal Component Analysis) Nedir?

Machine Learning projelerinde bazen veri setleri çok fazla feature (özellik) içerebilir. Çok fazla feature:

PCA (Principal Component Analysis)

In order we understand data we need to visualize first, but we can’t visualize so many dimensions at once, we can use 2 or 3 feature &…

What is the curse of dimensionality?

The curse of dimensionality is a concept in machine learning and data science that describes how problems become much harder as the number…

Dimensionality Reduction: A Beginner’s Guide to Principal Components Analysis (PCA)

Dimensionality reduction is actually a very easy concept to understand.

Life Is Too Short for Wrong Metrics: Visualizing Sparse Count Data with p-SNE

When was the last time you ran t-SNE on data full of zeros and actually got something useful? Have you ever had count data to visualize and…

Beyond Models: Why Modern Data Science Requires Systems Thinking

From forecasting and representation learning to deployment and real-world machine learning systems.

The Hidden Mathematics Behind Every AI Model You Use

Have you ever imagined how a subject that is often hated by children during their school years — considered boring and even feared — has…

Applying FAMD in Python: Preparing Mixed Data for Clustering

From data exploration to dimensionality reduction using the Prince library – building the foundation for K-Means clustering.

Dimensionality Reduction Techniques for Large-Scale Healthcare Analytics

The rapid digitization of healthcare systems has led to an explosion of high-dimensional data, including electronic health records (EHRs)…

MANAGEMENT OF CONDYLAR FRACTURES:A COMPARISION OF CLOSED REDUCTION AND TRANSPAROTID APPROACH

To compare Transparotid Reduction and Closed Reduction Approach in management of Condylar fractures for parameter like

PCA Explained Visually in 3D: From Data to Principal Components Step-by-Step

Understand the principal component analysis in five steps.

Unmasking PCA: From Linear Algebra to Predictive Modeling

In the age of high-dimensional data, we are often drowning in features but starving for information. Imagine you are trying to describe a…

Dimensionality Reduction

Dimensionality reduction is a technique used in machine learning to simplify a dataset by reducing the number of features while keeping the…

Less is More: How PCA and SVD Reveal What Actually Matters

While working with a feature heavy dataset, at first glance more data might feel like more information. But in practice, high-dimensional…

UMAP (Uniform Manifold Approximation and Projection)

Hello everyone 👋

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Hello everyone 👋

The Beautiful Math Behind PCA: Part2 : Singular Value Decomposition

A complete walkthrough of SVD from scratch — and how it makes PCA faster, cleaner, and more general.

The Art of Doing Less: A Practical Guide to Sparse Modeling and Estimation

In the era of “Big Data,” we often face an ironic problem: we have too much data, but specifically, too many dimensions.

Kümeleme ve Boyut indirgeme: Denetimsiz Öğrenmenin Sezgisel Anatomisi

Kümeleme ve Boyut İndirgeme Nedir?

t-SNE Explained: Visualising High-Dimensional Data

A beginner-friendly guide to how t-SNE turns high-dimensional data into intuitive 2D visualisations using probabilities and perplexity.

t-SNE

Manifold learning is a type of nonlinear dimensionality reduction that assumes high-dimensional data actually lies on a lower-dimensional…

The Lies UMAP Tells Us

Or: Why do our projections show clusters of the same size regardless of their original cardinality?

Dimensionality Reduction: Dive into PCA, t-SNE, and UMAP

ML Quickies #31

A global geometric framework for nonlinear dimensionality reduction. Science 2000

level: เปเปอร์ วารสาร Science เมื่อ 25 ปีที่แล้ว เคยเอามาศึกษาสมัยอยู่ปอตรี ตอนทำจบตรี ได้มา 5 พันจาก Exxon ชมเชย ตอนนั้นชมเชยนะ…

Understanding Dimensionality Reduction and Feature Selection in Machine Learning

In the age of big data and high-throughput analytics, modern machine learning pipelines often deal with datasets containing thousands of…

UML Part 9— Dimensionality Reduction

In today’s data-driven world, we deal with lots of data — sometimes with hundreds or thousands of features (columns). But here’s the catch

The Dimensional Odyssey: Navigating the Manifolds of t-SNE and UMAP

We thoroughly adventure the world of dimensionality reduction: What it is, Why it matters, and How tools like t-SNE and UMAP work.

Day 37: Advanced Visualization with t-SNE

Hello everyone, and welcome to Day 37 of “100 Days of Data Science”! Imagine you have a high-dimensional dataset — maybe thousands of gene…

Vector store and generation of embeddings inside database

Vector store is used to name a database that is able to store information in vector format (Vectors are mathematical representations of…

Understanding Dimensionality Reduction

Discover the importance of dimensionality reduction, its techniques, and how to apply them to image datasets while visualizing and…

SVD — Single Value Decomposition

Today, we embark on an exciting journey into the world of Singular Value Decomposition (SVD) — a fundamental concept in linear algebra…

Dimensionality Reduction Techniques

Linear Discriminant Analysis (LDA)

Discover how LDA helps identify critical data features

Simplifying Statistics and Dimensionality Reduction: Chi-Square Goodness of Fit and ISOMAP Made…

Picture a puzzle with a thousand pieces. How can you determine if your solution is the correct fit? Or, imagine a sea of data points. How…

Using Variational AutoEncoders (VAE) for Time-Series Data Reduction

With the rise of IoT devices, industrial sensors, and financial market data, time-series data is being generated at unprecedented rates…

Matrix Algebra: Foundation (Part 2)

Discover the profound impact of rank, eigenvalues, and eigenvectors, essential matrix concepts that underpin data science and engineering.

Protein Property Prediction with CNNs: A Technical Dive with Real-World BioData

Convolutional Neural Networks (CNNs) excel at uncovering valuable insights even from sparse biodata.

Navigating the Complexities of High-Dimensional Data with Isomap in Machine Learning

Delve into the world of Isomap (Isometric Mapping), a groundbreaking technique in machine learning that offers a new perspective on…

UMAP Technique Overview

A quick case study on my experience with dimension reduction studies.

Understanding UMAP (Uniform Manifold Approximation and Projection)

Revolutionizing Dimensionality Reduction in Machine Learning

The Power of Randomized SVD

Randomized SVD is a method for reducing the computational cost of singular value decomposition.

The Curse of Dimensionality in Data Analysis

In the realm of data analysis and Machine Learning, the curse of dimensionality poses challenges that become increasingly prominent as we…

Unraveling Data Patterns with Isomap: A Guide to Dimensionality Reduction — Part 4

Dimensionality reduction techniques play a crucial role in simplifying complex data, making it more manageable and informative for machine…

Exploring Isomap

A Powerful Technique for Dimensionality Reduction in Machine Learning