https://github.com/neonwatty/autograd_tutorials

A set of autograd tutorial notebooks
https://github.com/neonwatty/autograd_tutorials

autograd autograd-tutorials automatic-differentiation backpropagation jupyter-notebook lecture-notes

Last synced: 19 days ago
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A set of autograd tutorial notebooks

• Host: GitHub
• URL: https://github.com/neonwatty/autograd_tutorials
• Owner: neonwatty
• Created: 2018-12-01T14:07:49.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-16T00:41:16.000Z (over 5 years ago)
• Last Synced: 2024-10-10T19:16:11.627Z (about 1 month ago)
• Topics: autograd, autograd-tutorials, automatic-differentiation, backpropagation, jupyter-notebook, lecture-notes
• Language: Jupyter Notebook
• Homepage:
• Size: 4.75 MB
• Stars: 7
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# A few `autograd` tutorial notebooks  

This repo contains a set of Jupyter notebook describing how to use various [autograd](https://github.com/HIPS/autograd) functionalities, complementing the excellent tutorial located at the repo itself, including:

- [**basic_autograd_examples.ipynb**](https://nbviewer.jupyter.org/github/jermwatt/autograd_tutorials/blob/b6d264a62d3f3028406c76db4d3f476c6337fdff/basic_examples.ipynb) covering basic functionalities such as: derivative computation using standard and lambda functions, subtleties involved in automatic differentiation and the array of gradient prototypes provided by `autograd`, and computing partial derivatives of multi-input functions

- [**flattening_functions_using_autograd.ipynb**](https://nbviewer.jupyter.org/github/jermwatt/autograd_tutorials/blob/b775b089460e2204a5d37dcaada5e0842ca3f0de/flattening_functions.ipynb) covering usage of `autograd`'s [flatten_func](https://github.com/HIPS/autograd/blob/master/autograd/misc/flatten.py) function 

These notebooks were produced as supplementary material for the second edition of the textbook Machine Learning Refined, published Cambridge University Press, set for release in mid-2019.  You can find a host of examples employing `autograd` and - in particular - `flatten_func` on the main repository for the textbook [located here](https://github.com/jermwatt/mlrefined) (see for example the drafts on [multi-class classification](https://jermwatt.github.io/mlrefined/blog_posts/7_Linear_multiclass_classification/7_2_Perceptron.html) [fully connected networks](https://jermwatt.github.io/mlrefined/blog_posts/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons.html)).