Autoencoders are artificial neural networks that are designed to learn efficient data codings without any external supervision. They are commonly used for dimensionality reduction and to remove noise from data signals. As their name suggests, autoencoders learn to encode and then reconstruct original inputs with minimal error.
How Do Autoencoders Work?
Autoencoders consist of two main components: an encoder and a decoder. The encoder reduces the dimensionality of the input data and compresses
What is Independent Component Analysis (ICA)?
Independent Component Analysis (ICA) is a statistical and computational technique used to reveal hidden factors that underlie sets of random variables, measurements, or signals. It defines a generative model for the observed multivariate data provided as a large database of samples. In this model, the data variables are considered linear mixtures of some unknown latent variables, and the mixing system is also unknown. The latent variables are consid
What is a Latent Diffusion Model?
A Latent Diffusion Model is a type of machine learning algorithm that is used to analyze and understand data that is represented in a so-called "latent space". This space is built using Variational Autoencoders (VAEs) and is considered a lower-dimensional representation of the original data. The goal of the Latent Diffusion Model is to learn how information in the latent space diffuses over time.
How does a Latent Diffusion Model Work?
At a high level, the L
Introduction to Linear Discriminant Analysis (LDA)
Linear Discriminant Analysis (LDA) is a statistical method used in pattern recognition and machine learning to classify and separate two or more classes of objects or events. Originally developed by Sir Ronald A. Fisher in the 1930s, LDA is widely used in image recognition, bioinformatics, text classification, and other fields.
How Does Linear Discriminant Analysis Work?
The goal of LDA is to find a linear combination of features or variable
What is Parametric UMAP?
Parametric UMAP is a type of algorithm that helps us to better understand complex data sets by reducing their dimensionality. It's a way of simplifying the data so that it's easier to analyze and visualize. Dimensionality reduction is important because it allows us to work more efficiently with larger data sets, make better predictions, and understand the data in ways that would be impossible without this technique.
How does Parametric UMAP work?
Parametric UMAP exte
What is Principle Components Analysis (PCA)?
Principle Components Analysis (PCA) is a technique used in machine learning to reduce the dimensionality of data. Essentially, this means that PCA simplifies complex data by identifying groups of variables that are correlated and then combining those variables into a smaller, more manageable set of new variables called principle components or latent factors that still retain most of the original information.
How Does PCA Work?
PCA works by using a