[Feature Request]: Add Sparse Autoencoders in Machine Learning

[pavitraag]

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Feature Description

Sparse Autoencoders are a type of autoencoder designed to learn efficient representations of data by enforcing sparsity constraints on the hidden layer. This is achieved by adding a penalty term to the loss function, encouraging the model to activate only a small number of neurons at a time. Sparse Autoencoders are particularly useful for feature learning, anomaly detection, and data compression, as they focus on capturing the most significant patterns in the data.

Use Case

Incorporating Sparse Autoencoders into the project would enhance its ability to extract meaningful and sparse features from high-dimensional data. This feature is valuable for applications where data interpretability and reduced dimensionality are critical, such as image processing, fraud detection, and biological data analysis. By learning sparse representations, the project can improve its performance in identifying important structures within the data, leading to more accurate and efficient models for various machine learning tasks.

Benefits

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Priority

High

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[github-actions]

Hi @pavitraag! Thanks for opening this issue. We appreciate your contribution to this open-source project. Your input is valuable and we aim to respond or assign your issue as soon as possible. Thanks again!

[pavitraag]

@ajay-dhangar can u please assign me this

[github-actions]

Hello @pavitraag! Your issue #3758 has been closed. Thank you for your contribution!