Deep-Learning-From-Scratch-2

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deep-learning-from-scratch-2 is an open-source resource providing source code for the book "Deep Learning from Scratch 2" by O'Reilly Japan. It focuses on natural language processing and includes code examples for each chapter.

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Pricing

Open Source · Free

Free tier

Yes

API

Skill level

Technical

About

What is deep-learning-from-scratch-2?

deep-learning-from-scratch-2 is a comprehensive support site for the book "Deep Learning from Scratch 2 ―Natural Language Processing Edition" (O'Reilly Japan, 2018). This GitHub repository offers all the source code used throughout the book, making it an invaluable resource for readers looking to implement and experiment with the concepts discussed. The repository is meticulously organized by chapter, with dedicated folders for each, alongside common utilities and dataset-related code. It requires Python 3.x, NumPy, and Matplotlib, with optional support for SciPy and CuPy. The code is released under an MIT license, allowing for free commercial and non-commercial use. Additionally, the site provides links to errata and contact information for reporting new errors, ensuring the accuracy and usability of the learning materials.

Best used for

Ideal for students and professors who need to learn deep learning concepts, implement natural language processing algorithms, and study practical code examples. Especially valuable for those following the "Deep Learning from Scratch 2" textbook and seeking hands-on experience.

Common actions

learn deep learning

implement NLP algorithms

study source code

practice machine learning

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Capabilities

Key features

Chapter-wise source code
Natural language processing examples
Dataset code included
Python 3.x compatible
MIT licensed

Target Audience

studentprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source · Free

Free

FAQs

What software is required to run the source code?

To execute the source code, you will need Python 3.x, NumPy, and Matplotlib. Additionally, SciPy and CuPy are optional libraries that can be used for enhanced functionality, depending on the specific examples you wish to run.

Can I use the source code for commercial projects?

Yes, the source code in this repository is released under an MIT license. This license permits both commercial and non-commercial use, allowing you to freely integrate and adapt the code for your own projects.

Where can I find the learned weight files for chapters 6 and 7?

The pre-trained weight files, specifically for chapters 6 and 7, are not included directly in the GitHub repository. You can obtain these necessary files from the official O'Reilly Japan website for the book, at the provided URL.

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