Visual Studio Magazine

Data Dimensionality Reduction Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...
JSTOR Daily

Reducing the Dimensionality of Data with Neural Networks

High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can ...

Guangxi Power Grid Applies for Patent on API Data Stream Security Label Generation, Promoting Advances in Intelligent Monitoring Technology

In the new wave of digital transformation, Guangxi Power Grid Co., Ltd. has recently applied for a patent titled "An Intelligent Generation Method and System for API Data Stream Security Labels," with ...
Visual Studio Magazine

Data Dimensionality Reduction Using a Neural Autoencoder with C#

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...