https://github.com/akramz/grokking-deep-learning-notebooks

Notes & Code to go over "Grokking Deep Learning" Book by Andrew Trask
https://github.com/akramz/grokking-deep-learning-notebooks

artificial-intelligence backpropagation deep-learning federated-learning forward-propagation lstm machine-learning neural-networks numpy numpy-tutorial rnn sgd

Last synced: 18 days ago
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Notes & Code to go over "Grokking Deep Learning" Book by Andrew Trask

• Host: GitHub
• URL: https://github.com/akramz/grokking-deep-learning-notebooks
• Owner: Akramz
• Created: 2019-03-19T17:00:09.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-02-19T11:33:45.000Z (9 months ago)
• Last Synced: 2024-10-08T18:06:37.003Z (about 1 month ago)
• Topics: artificial-intelligence, backpropagation, deep-learning, federated-learning, forward-propagation, lstm, machine-learning, neural-networks, numpy, numpy-tutorial, rnn, sgd
• Language: Jupyter Notebook
• Homepage: https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/tree/master/
• Size: 34.5 MB
• Stars: 32
• Watchers: 2
• Forks: 16
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Grokking Deep Learning by Andrew W. Trask

> https://www.manning.com/books/grokking-deep-learning



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.

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.

    

> (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)).

# Roadmap

"Grokking Deep Learning" has 16 chapters. We provide links for the available notebooks:

1. Introducing Deep Learning: Why you should Learn It?

2. fundamental Concepts: How Do Machines Learn?

3. [Introduction to Neural Learning](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/03.forward_propagation.ipynb): Forward Propagation

4. [Introduction to Neural Learning](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/04.gradient_descent.ipynb): Gradient Descent

5. [Learning Multiple Weights at a Time](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/05.Generalizing_GD.ipynb): Generalizing Gradient Descent

6. [Building your first deep neural network](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/06.Back_propagation.ipynb): Introduction to Backpropagation

7. How to Picture Neural Networks: In your Head & on Paper

8. [Learning Signal & Ignoring Noise](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/08.Regularization_Batching.ipynb): Introduction to Regularization & Batching

9. [Modeling Probabilities & Non-Linearities](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/09.Probabilities_Non-Linearities.ipynb): Activation Functions

10. [Neural Learning about Edges & Corners](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/10.Intro_to_CNNs.ipynb): Introduction to Convolutional Neural Networks

11. [Neural Networks that Understand Language](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/11.NNs_that_Understand_Language.ipynb): King - Man + Woman == ?

12. [Neural Networks that write like Shakespeare](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/12.RNNs.ipynb): Recurrent Layers for Variable Length Data

13. [Introducing Automatic Optimization](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/13.DL_framework.ipynb): Let's build a deep learning framework

14. [Learning to Write like Shakespeare](https://nbviewer.jupyter.org/github/Akramz/grokking-deep-learning-notebooks/blob/master/14.LSTMs_LM.ipynb): Long Short-term Memory

15. Deep Learning on Unseen Data: Introducing Federated Learning

16. Where to Go from Here: A brief Guide

> Hidden Notebooks are mostly based on original content from the book.

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