In 2017, a groundbreaking paper introduced the transformer architecture, setting off a chain reaction that would reshape…
Math
The “Math” category is dedicated to exploring the mathematical foundations that underpin the field of artificial intelligence and machine learning. Here you’ll find resources that demystify key concepts from linear algebra, calculus, probability theory, and optimization, and illustrate their practical applications in building and training AI models. The materials cover essential topics like vector spaces, matrices, derivatives, gradients, and cost functions, and guide you through implementing numerical computations using popular libraries like NumPy and SymPy. You’ll learn how to formulate machine learning problems as mathematical optimization tasks, and understand the core principles behind techniques like gradient descent, backpropagation, and regularization. The category also dives into advanced topics like Bayesian statistics, information theory, and graph theory, and explores their connections to cutting-edge AI research. Whether you’re a student looking to build a strong mathematical foundation for your AI journey or a practitioner seeking to deepen your understanding of the theoretical underpinnings of machine learning, these resources will equip you with the knowledge and intuition to navigate the mathematical landscape of AI with confidence and apply it effectively in your projects.