Implementing a Multilayer Perceptron for MNIST Classification
Published:Jan 5, 2026 06:13
•1 min read
•Qiita ML
Analysis
The article focuses on implementing a Multilayer Perceptron (MLP) for MNIST classification, building upon a previous article on logistic regression. While practical implementation is valuable, the article's impact is limited without discussing optimization techniques, regularization, or comparative performance analysis against other models. A deeper dive into hyperparameter tuning and its effect on accuracy would significantly enhance the article's educational value.
Key Takeaways
- •The article implements a Multilayer Perceptron (MLP).
- •The task is MNIST handwritten digit classification.
- •It builds upon a previous logistic regression implementation.
Reference
“前回こちらでロジスティック回帰（およびソフトマックス回帰）でMNISTの0から9までの手書き数字の画像データセットを分類する記事を書きました。”