- latest version available from: https://github.com/miroenev/teach_DL , prerequisites:
* Matplotlib, Numpy, Keras, and K3D for realtime training 3D surface visualization
* TensorFlow as the Keras backend for NN graph (queries model weights)
A video walkthrough of this notebook is available on YouTube.
Lets try to predict commute duration from two observable independent variables: the time of day and the weather conditions.
In this toy example we'll first take on the role of the 'traffic gods' and decree that commute duration is defined through a linear mixture of the two independent variables. Later we'll sample from the distribution defined by these variables and generate a training dataset. This sampling procedure will be analogous to keeping a journal of all of our commutes for some [ long ] period of time, where each log entry consists of a set of
- X: [ time-of-departure, weather-condition ], and the associated
- Y: [ commute-duration ].
Given such a journal [dataset], we'll split it into training (75%) and testing (25%) subsets which we'll use to train and evaulate our model respectively. Specifically, we'll build a neural network model whose weights are initially randomly initialized, but are trained/updated as we stream the training data through (via the backpropagation learning algorithm). Each update will get us closer to having a model that has learned the relationship between X and Y or ([ time-of-departure, weather-condition ] to [ commute-duration] ).
During the training process we'll try to visualize the network's behavior by asking it to predict all the entries in our logbook using its current parameters/weights. As the training process unfolds, you should be able to see how the network adapts itself to the target surface/function that we determined for the commute duration.
