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).