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| -### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ### |
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| -# file to edit: index.ipynb |
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| -# command to build the docs after a change: nbdev_build_docs |
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| ---> |
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| -# Notebooks — Grokking Deep Learning by Andrew W. Trask |
| 1 | +# Grokking Deep Learning by Andrew W. Trask |
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13 | 3 | > https://www.manning.com/books/grokking-deep-learning
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14 | 4 |
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15 | 5 | <div style="text-align:center"><img style="width:100%" src="static/imgs/Cover.png"></div>
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16 | 6 |
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| -- Grokking Deep Learning was written to help **give you a foundation in deep learning** so that you can **master a major deep learning framework**. |
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| - - Requires no math background beyond basic arithmetic. |
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| - - Doesn't rely on a high-level library that might hide what's going on. |
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| - - Anyone can read this book and understand how deep learning really works. |
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| - - You won't just read the theory, you'll discover it yourself. |
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| - > (You can Buy the Book from [Manning Publications](https: //www.manning.com/books/grokking-deep-learning) or [Amazon](https://www.amazon.com/Grokking-Deep-Learning-Andrew-Trask/dp/1617293709)). |
| 7 | +This repo is home to notes & the code that accompanies Andrew W. Trask's "Grokking Deep Learning" book. It provides a solid foundation in deep learning so that you can master any major deep learning framework. |
| 8 | + |
| 9 | +The book requires no math background beyond basic arithmetic. It doesn't rely on a high-level library that might hide what's going on. Anyone can read the book and understand how deep learning really works. You won't just read the theory, you'll discover it yourself. |
| 10 | + |
| 11 | +> (You can Buy the Book from [Manning Publications](https: //www.manning.com/books/grokking-deep-learning) or [Amazon](https://www.amazon.com/Grokking-Deep-Learning-Andrew-Trask/dp/1617293709)). |
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24 | 13 | # Roadmap
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25 | 14 |
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| -"Grokking Deep Learning" has 16 Chapters: |
| 15 | +"Grokking Deep Learning" has 16 chapters. We provide links for the available notebooks: |
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28 | 17 | 1. Introducing Deep Learning: Why you should Learn It?
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29 | 18 | 2. fundamental Concepts: How Do Machines Learn?
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