Neural Networks

It took a 6th grade science fair for me to train my first neural network in a long while. My family lives in rural patch of Northern California filled with oak, redwood and other native flora. For the upcoming science fair my son decided to build a machine learning model…

Technology is all about making things faster and easier. Thanks to our computers, we don’t need to do math, design, or experiment when it comes to science. One area that really benefits from the great abilities of computers is nanotechnology. Nanotechnology is working on materials at atomic scales. This…

In 1950, Alan Turing asked, “Can machines think?” Since then many have been working nonstop in order to find an answer to this question. Despite their hard work, there are still some aspects that haven’t yet been explored in computer vision literature or that dont have a concrete answer. For…

Building blocks for Memory Augmented Neural Networks used for One-Shot Learning — In my previous parts, I have discussed the basics of One-Shot learning alongside how, using Transfer Learning & Contrastive loss function, Siamese Network implements One-Shot Learning. It’s time to go a little complex, Neural Turing Machines. Sounds like some missile !! Neural Turing Machines themselves don’t implement One-Shot Learning but…

Regularization is an approach to address over-fitting model. Overfitted model fails to generalize estimations on test data. When the underlying model to be learned is low bias/high variance, or when we have small amount of data, the estimated model is prone to over-fitting. Regularization reduces the variance of the model.

Neural Network Architecture, Self-learning, Board Symmetries, Playout Cap Randomization and other pieces to make it all work. From our previous parts, we now understand how Monte Carlo Tree Search (MCTS) works and how we can use it to get an output policy, given a board state. We also understand the…

This article tries to explain how to create a basic keras based ANN model. …