In this notebook, we attempt to conceptualize Deep Neural Networks (DNN) and apply one to a common problem. We'll train a version of a DNN called a Multilayer Perceptron (or vanilla network) to classify images from the MNIST database. The MNIST database contains 70,000 hand-written digits from 0-9 and is one of the most famous datasets in machine learning. If this all sounds confusing so far, don't worry, we'll start at the beginning.
This notebook requires Python 3.
Dependencies found in requirements.txt
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- Run the notebook
jupyter notebook dnn_mlp_mnist.ipynb