What is this?
Code samples for Michael Nielsen's book 'Neural Networks and Deep Learning' — a set of Python programs meant to be read and run while working through the book.
Code samples for Michael Nielsen's book 'Neural Networks and Deep Learning' — a set of Python programs meant to be read and run while working through the book.Is this the book itself, or code that goes with it?Just the code. The book text lives elsewhere; this repo holds the runnable example programs that accompany it, so you read the book and run these samples alongside.
Just the code. The book text lives elsewhere; this repo holds the runnable example programs that accompany it, so you read the book and run these samples alongside.How do I run the samples?Clone the repository and run the Python scripts directly — there is no build or install step. Just keep in mind the code targets Python 2.6 or 2.7.
Clone the repository and run the Python scripts directly — there is no build or install step. Just keep in mind the code targets Python 2.6 or 2.7.What do I need installed?Python 2.6 or 2.7. network3.py additionally needs Theano 0.6 or 0.7 — later Theano releases would require code changes the author is not making.
Python 2.6 or 2.7. network3.py additionally needs Theano 0.6 or 0.7 — later Theano releases would require code changes the author is not making.Will this run on a modern Python setup?Not from this repo — it is intentionally frozen: no Python 3 compatibility update and no new features are planned, so expect 2012-era tooling.
Not from this repo — it is intentionally frozen: no Python 3 compatibility update and no new features are planned, so expect 2012-era tooling.Can I reuse the code in my own projects?Yes — it is under the MIT license, so you can use, copy, modify and redistribute it, even commercially, as long as the copyright notice is kept.
Yes — it is under the MIT license, so you can use, copy, modify and redistribute it, even commercially, as long as the copyright notice is kept.Is the project still maintained?Not actively — the author considers the code complete in its role as a companion to the book, so no new features are coming. Bug reports are still welcome, and you are encouraged to fork and extend the code yourself.
Not actively — the author considers the code complete in its role as a companion to the book, so no new features are coming. Bug reports are still welcome, and you are encouraged to fork and extend the code yourself.Where can I get a version that runs on Python 3?The README links a separate port of the code updated for Python 3.8-3.10. This repository itself stays on Python 2.6/2.7 and will not be updated.
The README links a separate port of the code updated for Python 3.8-3.10. This repository itself stays on Python 2.6/2.7 and will not be updated.What are similar projects?100-Days-Of-ML-Code, pytorch-tutorial, awesome-deep-learning, DeepLearning, TensorFlow-Course, pytorch-handbook, Coursera-ML-AndrewNg-Notes, fastbook
100-Days-Of-ML-Code, pytorch-tutorial, awesome-deep-learning, DeepLearning, TensorFlow-Course, pytorch-handbook, Coursera-ML-AndrewNg-Notes, fastbookWhich topics is it in?deep-learning-tutorial, neural-networks, study-guide, examples, neural-network, book, learning-resources, learning, tutorial, deep-learning, machine-learning, education
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