Machine Learning systems are often configured around Graphics Processing Units (GPUs) rather than Central Processing Units (CPUs). Why should this be the case, in an era when CPUs are powerful and (relatively) inexpensive? This article provides some insights into what GPUs are and why they provide advantages for certain types of computations, including some commonly used for machine learning and modeling.
There are a lot(!) of free online resources available if you want to learn practical machine learning skills, workflows, and processes. This post highlights some recommendations by Thomas Martin, an AI/ML Software Engineer at the Unidata,
The following are a few of Unidata AI/ML developer Thomas Martin's favorite books on machine learning. These books range from pure theory to hands-on practical Python programming help. They are arranged roughly in order if you are starting your journey into ML, but can also be read out of order.
