Recently, I made an educational simulation project on what is known as Particle Life to showcase how complexity can arise from simplicity. Particle Life is like Conway’s game of life but in Conway’s game of life the effect of the particles are confined to their surrounding neighbors only while in these simulations particles have effects…
As someone who came to the Machine Learning world from a Medical background, I couldn’t help not relating being stuck at a Local Maximum to other life situations. So I have decided to make a simulation project that helps to visualize this problem from a biological and also a political perspective where liberals and conservatives…
Artificial Neural Networks (ANNs) are powerful inference tools. They can be trained to fit complex functions and then used to predict new (unseen) data outside their training set. Fitting the training data is relatively easy for ANNs because of their Universal Approximation capability. However, that does not mean ANNs can learn the rules as we…
Genetic Algorithm (GA) and Stochastic Gradient Descent (SGD) are well-known optimization methods and are used for learning in Neural Networks. There are various implementations of GA, however, most of them (e.g. Neat) are not directly comparable to SGD because these GA methods use point/localized mutations in their connections/weights. Geoffrey Hinton, in one of his videos…