From recognizing speech to training virtual personal assistants to converse naturally; from detecting lanes on the road to teaching autonomous cars to drive; data scientists are taking on increasingly complex challenges with AI. Solving these kinds of problems requires training exponentially more complex deep learning models in a practical amount of time.
This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.
"DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches."
Using Keras to train a convolutional neural network on images from the car's cameras as well as steering angles from human driving. Using just those 2 data points, it'll be able to drive itself on any road.