The Q-learning algorithm implemented from scratch in Python — This article is a continuation of a series of articles about Reinforcement Learning (RL). Check out the other articles here: First Steps in the World Of Reinforcement Learning using Python Original Python implementation of how to find the best places to be in one of the fundamental worlds of reinforcement…towardsdatascience.com Temporal Differences with Python — First Sample-Based Reinforcement Learning Algorithm Coding up and understanding the TD(0) algorithm using Pythontowardsdatascience.com

Coding up and understanding the TD(0) algorithm using Python — This is a continuation article from my previous article: First Steps in the World Of Reinforcement Learning using Python Original Python implementation of how to find the best places to be in one of the fundamental worlds of reinforcement…towardsdatascience.com In this article, I want to familiarize the reader with the sample-based algorithm logic in Reinforcement Learning (RL). …

Original Python implementation of how to find the best places to be in one of the fundamental worlds of reinforcement learning — the grid world — The purpose of this article is to present fundamental concepts and definitions in Reinforcement Learning (from here on — RL) using Python code and comments. The article was heavily inspired by the great RL course: https://www.coursera.org/learn/fundamentals-of-reinforcement-learning

How to use big data on a small computer using Tensorflow, Python and iterators — The amount of data in the current business world is increasing every day. There are new data sources to merge, more rows to append and new columns to concatenate. Unfortunately, for a typical data scientist or a machine learning engineer, the pace at which one can buy a new laptop…

How to make an equivalent elastic net regression between sklearn and Tensorflow in Python — This article is intended for the practitioners who want to compare the sklearn and Keras implementation of elastic net regression. Mainly, how to go from Sklearn loss function to Keras (Tensorflow) loss function. Main parts of the article: A brief introduction to regularization in regression. Sklearn implementation of the elastic…

A complete Python implementation and explanation of the calculations behind measuring feature importance in tree-based machine learning algorithms — The aim of this article is to familiarize the reader with how are the importance of features calculated in decision trees. Personally, I have not found an in-depth explanation of this concept and thus this article was born. All the code used in this article is publicly available and can…

Coding and explaining in depth the very popular and competition-winning gradient boosting algorithm using Python — The aim of this article is to explain every bit of the popular and oftentimes mysterious gradient boosting algorithm using Python code and visualizations. …

BubbleSort, SelectionSort, InsertionSort, MergeSort and QuickSort algorithms are explained in detail using code and visualizations — This article is a continuation of one of my previous articles: https://medium.com/codex/algorithms-with-python-introduction-97f3bf3cf247. It will be easier to follow the ideas presented in this article after reading the one provided above. In this article, I will present my coded implementations for the following popular sorting algorithms:

An introductory article to algorithms that showcases the bubble sort algorithm in Python — No matter the programming language, a key concept in all of them are algorithms. Programming languages come and go, but the theoretical and practical sides of algorithms stay the same. …