Data Science

Genetic algorithms (GAs) are inspired by biology where only the fittest genes survive. It is based on Charles Darwin’s Natural Selection theory. We start with 2 parent chromosomes that each contain an ordered set of genes. Each parent contributes some of their genes when they mate to create children chromosomes. There is a randomness to the mating process so that each child has a diverse set of genes. This diversity is created by the crossover and mutation processes. Over time and with sufficient genetic diversity, the fittest genes, representing optimal characteristics for the species to survive, be come dominant and are propagated. This is nature’s way of optimizing over genetic diversity and we can co-opt this approach for tackling other optimization problems. ...

The purpose of this article is to share what I learned from a recent PyTorch course. We will share general machine learning tips as well as insights specific to deep learning library PyTorch. PyTorch is a deep learning library for Python and was created by Facebook in 2016. PyTorch is good for deep learning beginners. There are several other popular deep learning frameworks such as TensorFlow, Keras, Chainer, and ONNX. TensorFlow was developed by Google and now includes Keras (previously a separate framework). I chose to deepen my knowledge in PyTorch because it is easy to learn and is commonly used for deep learning. Fastai is another deep learning open source library that is really awesome and intuitive to use. While using Fastai, I found myself curious about the inner workings so it’s a plus that fastai wraps PyTorch as I can gain a better understanding of both libraries at once. Two birds, one stone! ...