Deep Learning Diary: Calculating Gradients in a Single-Layer Neural Network
Published:Jan 11, 2026 10:29
•1 min read
•Qiita DL
Analysis
This article provides a practical, beginner-friendly exploration of gradient calculation, a fundamental concept in neural network training. While the use of a single-layer network limits the scope, it's a valuable starting point for understanding backpropagation and the iterative optimization process. The reliance on Gemini and external references highlights the learning process and provides context for understanding the subject matter.
Key Takeaways
- •The article focuses on calculating gradients for a single-layer neural network.
- •It utilizes a specific book ('ゼロから作るDeepLearning') as a reference.
- •The development environment includes VScode, Python, and Anaconda.
Reference
“Based on conversations with Gemini, the article is constructed.”