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Deep Learning

  • 1_notmnist: Preprocess notMNIST data and train a simple logistic regression model on it.
  • 2_fullyconnected: Train a fully-connected network using Gradient Descent and Stochastic Gradient Descent.
  • 3_regularization: Use regularization techniques to improve a deep learning model.
  • 4_convolutions: Design and train a Convolutional Neural Network.
  • 5_word2vec: Train a skip-gram model on Text8 data and visualize the output.
  • 6_lstm: Train a Long Short-Term Memory network to predict character sequences.

Install

This project requires Python 2.7 and the following Python libraries installed:

You will also need to have software installed to run and execute an iPython Notebook

Code

Template code is provided in the following notebook files:

  • 1_notmnist.ipynb
  • 2_fullyconnected.ipynb
  • 3_regularization.ipynb
  • 4_convolutions.ipynb
  • 5_word2vec.ipynb
  • 6_lstm.ipynb

Run

In a terminal or command window, navigate to the top-level project directory notMNIST/ (that contains this README) and run one of the following commands:

ipython notebook 1_notmnist.ipynb 
jupyter notebook 1_notmnist.ipynb

This will open the iPython Notebook software and project file in your browser.

Data

Notebook #1 to #4 use the notMNIST dataset to be used with python experiments. This dataset is designed to look like the classic MNIST dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST.

Notebook #5 uses the Text8 dataset.

License

The contents of this repository are covered under the MIT License.