Understanding Reinforcement Learning

Reinforcement learning refers to machine learning focused on algorithms that learn how to interact with an environment. An example of such an algorithm is called Q-learning. Although it is closer to a brute force approach, Q-learning is probably the most popular reinforcement learning method. Before we get into Q-learning, let's first discuss why we don't use an unsupervised or supervised learning approach.

In a supervised learning approach, you give the algorithm rows of data, and each row has an answer that the algorithm will try to fit. It’s the equivalent of providing the algorithm pictures of dogs and cats, and…

a lot of people use Cython to compile their python code in C for performance gains. It’s petty easy to do and might be something that you want to take advantage of.

Make sure you pip install Cython first.

The overall process goes like this.

• Bring the parts of your code you want to convert to c into a separate file
• Give type information and let Cython know what you want to use in Python and what you don’t.
• compile the separate file using a setup.py file

For this example, let’s say we want to compile this function into C

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I think that a cool thing to learn as a beginner in python is how to produce random numbers. Random number generation is an interesting topic to get into learn simply because of its applications. This post will go over surface-level concepts of random number generation and the different ways we can go about using them with examples.

Computers are deterministic, meaning that if you put an input you will get the same output no matter how many times you try. This is because computers take inputs then follow sets of instructions the give you your output. So how do…