## Brief Descriptions

In theses articles, published on **Towards Data Science** and **freeCodeCamp**** **I** **discuss several topics including:

How to understand the effectiveness of machine learning optimization algorithms, such as stochastic gradient descent, from the perspective of theoretical physics (

**link**)The connections between the well-known Kolmogorov-Arnold theorem, from real analysis, and the impressive generalization power of artificial neural networks (

**link**)The reasons why artificial neural networks can predict the outcomes of almost any process in nature (

**link**).How restricted Boltzmann machines (RBMs), building blocks of deep neural networks, can be used to compute the state of lowest energy of certain kinds of quantum systems (

**link**).A detailed description of the interconnections between deep learning and renormalization group theory (

**link**).