This project contains the implementation of a Recursive Neural Network (RNN) in Python using only numpy and no other high level machine learning/neural network APIs or libraries. It is the first in a series of projects, and is developed with an aim to gain a deeper understanding of the fundamental concepts involved in designing and training RNNs.
In this project, a RNN is trained on a corpus of characters to enable it to predict the next character given previous characters.
Only 3 dependecies for the code -
- Python 2.7
- numpy
- jupyter
Install Jupyter notebooks, navigate to the directory containing numpy-RNN.ipynb in the command terminal and run the notebook by typing -
$ jupyter notebook numpy-RNN.ipynb
This will open the notebook in a browser. Execute each cell in the notebook one by one.
This code was written using Andrej Karpathy's code, and his blog post.
Copyright (c) 2016, Damien Henry All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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