Dimensionality Reduction

Big Data, Big Models: How to Train and Optimize Large Scale Sparse Models

Introduction Big data has become an integral part of modern business and research, with vast amounts of information being collected, analyzed, and stored every day. With the increasing volume of data, the need for more powerful models to analyze it has also grown. However, training large scale models can be a challenging task, especially when …

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Manifold Learning for Nonlinear Dimensionality Reduction

Manifold learning is a class of techniques used to reduce the dimensionality of high-dimensional data. It does this by identifying and representing the underlying structure of the data in a lower-dimensional space, known as a manifold. These techniques are particularly useful for visualizing and analyzing complex datasets that cannot be easily understood in their raw …

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