Genann VS btsk

> I am very much a beginner in the space of machine learning

While the (precious and useful) advice around seem to cover mostly the bigger infrastructures, please note that

you can effectively do an important slice of machine learning work (study, personal research) with just a battery-efficiency-level CPU (not GPU), in the order of minutes, on a battery. That comes before going to "Big Data".

And there are lightweight tools: I am current enamoured with Genann («minimal, well-tested open-source library implementing feedfordward artificial neural networks (ANN) in C»), a single C file of 400 lines compiling to a 40kb object, yet well sufficient to solve a number of the problems you may meet.

https://codeplea.com/genann // https://github.com/codeplea/genann

After all, is it a good idea to have tools that automate process optimization while you are learning the deal? Only partially. You should build - in general and even metaphorically - the legitimacy of your Python ops on a good C ground.

And: note that you can also build ANNs in R (and other math or stats environments). If needed or comfortable...

Also note - reminder - that the MIT lessons of Prof. Patrick Winston for the Artificial Intelligence course (classical AI with a few lessons on ANNs) are freely available. That covers the grounds relative to climb into the newer techniques.

Genann - Simple ANN in C89, without additional dependencies. Zlib

AiGameDev.com implementation: (https://github.com/aigamedev/btsk/blob/master/BehaviorTree.cpp)