What is deep-learning-from-scratch?

This repository holds the companion source code for the Japanese-language book "Deep Learning from Scratch" (ゼロから作る Deep Learning), published by O'Reilly Japan in 2016. The book teaches how neural networks and deep learning work by building them from the ground up in Python, without relying on high-level machine learning frameworks. The code is organized into folders matching the book's chapters, from chapter 1 through chapter 8, plus shared utility code and dataset handling. Readers work through each chapter's folder alongside the text. Running the examples requires Python 3, NumPy (a library for numerical calculations), and Matplotlib (a library for drawing charts). The README is written in Japanese, as the book is aimed at a Japanese-speaking audience. For readers who do not want to set up a local Python environment, the repository provides direct links to run each chapter's notebooks on Amazon SageMaker Studio Lab, a free cloud computing environment from AWS. You sign up with an email address and can run the code in a browser without installing anything locally. The code is released under the MIT license, meaning it can be freely used for personal or commercial purposes. An errata page on the repository's wiki tracks corrections to the book's printed text, and errors not listed there can be reported to O'Reilly Japan directly by email. This is a reading companion, not a standalone tool. Its value is in following the book chapter by chapter to understand how deep learning algorithms work at a foundational level.