Wednesday, June 24, 2015
Wednesday, June 17, 2015
Monday, May 11, 2015
The age-old debate about neural networks (both artificial and biological) is whether they have a grandmother cell, a neuron cell/node somewhere in the net that is activated when one's grandmother is viewed (assuming a biological vision scenario or computer vision application).
For biological neural networks, the jury is out, and the answer is leaning toward the "no" side. For artificial neural networks, if you Google for the answer, you'll almost always come across the admonishment to avoid grandmother cells in your neural networks. But to beginners to neural networks, this advice can be easily misunderstood.
More precisely, grandmother cells are to be avoided in the internal nodes. The reason is that internal nodes are supposed to be for latent variables, i.e., intermediate properties like "big eyes" or "big teeth". If an internal node is already recognizing grandma, then that is an indication of overfitting and that perhaps the neural network was created too large for the amount of training data or search space.
But all artificial neural networks whose job it is to classify images where one of the classes is grandma will have a grandmother cell, namely in the output layer. That's simply how you get your output from a classifier artificial neural network.
Thursday, May 7, 2015
The above is an animated interpretation of one of the (static) images from chapter 3 of my upcoming book Spark GraphX In Action. Chapter 3 is now available for download for those who have purchased the MEAP. Today, May 7, 2015, the MEAP is available for a 50% discount using the discount code dotd050715au.
Thursday, March 12, 2015
My new book became available on MEAP on March 6, 2015, and the print version is expected later in 2015.
Spark and Graphs are two tools rapidly being adopted by Data Scientists. Combine them and you get the GraphX component of Spark. Written in a way that assumes minimal Scala, the book quickly brings readers up to speed on all four: Scala, Spark, definitions from graph theory, and GraphX itself. An appendix provides three quick ways to get Spark running, and the chapters provide explicit examples from the most basic up through machine learning.