Guide to real Machine Learning applications

This series of articles dives deeper into the actual applications of Machine Learning that are currently in use in many current technological processes and devices.

Amazon Alexa

Through these posts entitled “Machine Learning is Fun!”, Adam Geitgey guides us step by step through the concepts, data, algorithms, code, results and pitfalls of machine learning applications from image, face and speech recognition to language translation and more. It also gathers several different sources for more details on each application and its development.

Image encoding

This series is really dense with detailed code, but it is also explained very clearly, step by step, with detailed illustration. It notably covers the use of a Convolutional Neural Network (including Generative Adversarial Network) and Recurrent Neural Network, together with some of their most prominent applications in daily life. It is a real course not to be missed for any ML developer!

Here is the list of posts with direct links:

9. Constraints: Visual Object Recognition

Borders and faces orientation

Discerning borders of objects and face orientation with vectors, an initial computer vision theory seemed plausible but too difficult to implement.

Orthographic projection

In orthographic projection, the correspondence of a system of points of three known objects and one unknown object creates a system of equations with a unique solution of parameters. If this solution can be applied  to all points of the unknown object, the object is recognized.

This works with manufactured objects but not so well with natural objects.

“Goldilocks principle”

Don’t search for features that are too big / complex, and not too small / simple. Not too big, not too small.

Face recognition

Use an integral of different recognizable points on a face: search for a correlation of 2 eyes + 1 nose, 1 nose + 1 mouth, etc.