A comprehensive guide to the most critical machine learning mathematical equations, from probability theory to advanced concepts like diffusion models and attention mechanisms. It includes theoretical explanations and practical Python implementations.

A highly technical yet understandable exploration of backpropagation, detailing its mechanics, mathematical foundations, and practical applications, making it accessible to those with a basic understanding of calculus and machine learning.

A post-COVID-19 critical review of Robert Bryce’s “Power Hungry: The Myths of 'Green' Energy and the Real Fuels of the Future.”

In this blog post, we’ll dive deep into the implementation of a character-level language model using a vanilla Recurrent Neural Network (RNN). This type of model can learn to generate text one character at a time, capturing the patterns and structure of the language it’s trained on. We’ll walk through…

This blog post presents my detailed implementation of Andrej Karpathy’s Neural Networks: Zero to Hero YouTube lecture series and exercises in Jupyter Notebook. The articles go deeply from NNs to MLPs to CNNs to LLMs to ensure a thorough and robust understanding of neural networks.

A pre-COVID-19 critical review of Robert Bryce’s “Power Hungry: The Myths of 'Green' Energy and the Real Fuels of the Future.”