Code in 3 programming languages: Java, Python and Swift.
Build nodes and data models for linear regression.
Use summarizing mechanisms to handle text data.
Test projects on mobile devices.
Examine computational graphs.
Analyze scalars and histograms.
Build neuron functions.
Load, convert, and display image and digit data.
Describe data with statistics.
And much more....
Learn How Models Work
Computational graphs consist of a network of connected nodes (often called neurons). Each of these nodes typically has a weight and a bias that helps determine, given an input, which path is the most likely.
There are 4 main components to building a machine learning program: data gathering and formatting, model building, training, and testing and evaluating
Data Gathering and Formatting
You will learn to gather plenty of data for the model to learn from.
All data should be formatted pretty much the same (images same size, same color scheme, etc.) and should be labelled. Also divide data into mutually exclusive training and testing sets.
You will learn to figure out which kind of model scheme works best and what kinds of algorithms work best for the problem you’re trying to solve.
Training, Testing and Evaluating
The model can choose paths through the neural network or computational graph based upon the inputs for a particular run, as well as the weights and biases of neurons in the network.