# -*- coding: utf-8 -*-
"""Deep Learning with Neural Networks and TensorFlow.

Deep learning is part of a broader family of machine learning methods based on
learning data representations, as opposed to task-specific algorithms. Learning
can be supervised, partially supervised or unsupervised.

A deep neural network (DNN) is an artificial neural network with multiple
hidden layers between the input and output layers.

We will use the library Tensorflow to do number crunching. A package like
TensorFlow allows us to perform specific machine learning number-crunching
operations tensors with large efficiency. We can also easily distribute this
processing across our CPU cores, GPU cores, or even multiple devices like
multiple GPUs. But that's not all! We can even distribute computations across
a distributed network of computers with TensorFlow.

Example:

        $ python regularDeepLearningWithNeuralNetworks.py

Todo:
    *
"""

Add Deep Learning with Neural Networks and TensorFlow
Add Deep Learning with Neural Networks and TensorFlow
