Implementing a 2-Layer Neural Network for MNIST with Numerical Differentiation
Published:Jan 12, 2026 16:02
•1 min read
•Qiita DL
Analysis
This article details the practical implementation of a two-layer neural network using numerical differentiation for the MNIST dataset, a fundamental learning exercise in deep learning. The reliance on a specific textbook suggests a pedagogical approach, targeting those learning the theoretical foundations. The use of Gemini indicates AI-assisted content creation, adding a potentially interesting element to the learning experience.
Key Takeaways
- •Focuses on implementing a 2-layer neural network.
- •Utilizes numerical differentiation for the implementation.
- •Employs the MNIST dataset for training and evaluation.
Reference
“MNIST data are used.”