A common way to solve a complex computing task is to chain together specialized components. In data-science this is the pipeline approach. Each component mostly treats the other components as I/O black-boxes. As developers we potentially have the full picture but the system does not. With Neural Network what happens between I and O is often too interesting to be ignored.
This is how simple neurons get smarter and perform so well for certain problems such as image recognition and playing Go. Inception: an image recognition model published by Google (From: Going deeper with convolutions, Christian Szegedy et al.) Some published examples of visualization by deep networks show how they're trained to build the hierarchy of recognized patterns, from simple edges and blobs to object parts and classes. In this article, we looked at some TensorFlow Playground demos and how they explain the mechanism and power of neural networks. As you've seen, the basics of the technology are pretty simple.
Mobile developers are often asked to implement the latest available features for their platforms, and demand for ML models in production application has increased dramatically over the last couple of years. The creation of production-ready Neural Networks requires a big dataset and lots of time, so our models in this course will have some reasonable limitations. But you will be able to train Semantic Segmentation Neural Network fast and understand critical concepts of how these models are trained and how they can be integrated into your apps. In this tutorial, we will look at the "Wanna Nails" case, and we will show you how to train a model that will detect nails in a couple of hours. "Wanna Nails" is an app that uses Object Segmentation to detect nails and try on different polish colors.
The idea was born to use TensorFlow/machine learning to automatically analyze these signals and using it to retrieve the PIN entered into the device - out of thin air! The setup for finding and recording such a signal can range from very simple up to very complex, for this case everything was done using Software Defined Radios. A cheap RTL-SDR receiver is available for roughly $30, though a more sophisticated device such as a HackRF or a bladeRF offer significantly higher sample rates (and a higher ADC resolution). Even with this cheap setup, the signal could be picked up from more than 2 meters (6.5 feet) away - using a directional antenna (and maybe using emissions on a different frequency band) this range can be easily increased. It was also found that connecting the USB cable to the device increases the measured strength of the emissions significantly.
If you've been following along with this series of blog posts, then you already know what a huge fan I am of Keras. Keras is a super powerful, easy to use Python library for building neural networks and deep learning networks. In the remainder of this blog post, I'll demonstrate how to build a simple neural network using Python and Keras, and then apply it to the task of image classification. To start this post, we'll quickly review the most common neural network architecture -- feedforward networks. We'll then discuss our project structure followed by writing some Python code to define our feedforward neural network and specifically apply it to the Kaggle Dogs vs. Cats classification challenge.