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