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@article{182479,
        author = {Pritika Mehra and Mini Singh Ahuja},
        title = {Exploring Linear Neural Networks and Autoencoders: Theory, Implementation, and GitHub Integration},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {2397-2398},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182479},
        abstract = {Linear neural networks and autoencoders provide essential insights into the fundamental structure of deep learning models. This paper investigates the theoretical background of linear neural networks and linear autoencoders, evaluates their performance on benchmark datasets, and demonstrates implementation using PyTorch. All experiments, datasets, and model checkpoints are hosted in a publicly available GitHub repository to ensure reproducibility and collaborative research. Results indicate that linear autoencoders closely approximate PCA-based dimensionality reduction, while linear neural networks excel in linearly separable classification tasks.},
        keywords = {Autoencoder, GitHub, Linear Neural Network, PyTorch, Unsupervised Learning},
        month = {July},
        }