Mahan

This blog post unravels the magic behind GPS, revealing that it’s not about maps but about ultra-precise timekeeping. It explains in simple terms how your device uses time signals from multiple satellites to find your location, then dives into the real-world engineering and mind-bending physics (like Einstein’s relativity) required to make it work, before finally unveiling the complex mathematics that powers it all.

This series explores DenseNet’s revolutionary approach to neural connectivity that solved vanishing gradients and improved feature reuse, examines its mathematical foundations and practical implementation, and discusses how its limitations eventually paved the way for Vision Transformers. We trace the evolution from convolutional networks to hybrid architectures, showing how each innovation built upon previous breakthroughs while addressing their shortcomings in the endless pursuit of more efficient and powerful deep learning models.

The Ackermann function is a deceptively simple algorithm that stands as a landmark in theoretical computer science. Defined by a concise set of recursive rules, it generates numerical values that grow at a rate faster than any primitive recursive function, quickly reaching magnitudes that are physically incomputable. While its naive implementation serves as a classic example of a recursion depth stress test, its true importance is historical and philosophical.

ResNet model and the seminal paper, Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, which won the Best Paper award at CVPR 2016. It is one of the most influential and fundamental papers in the history of deep learning for computer vision.

VGGNet is a famous deep learning model used in computer vision—essentially, teaching computers to understand images. It was created by researchers at the Visual Geometry Group (VGG) at the University of Oxford. Since its debut in 2014, VGGNet has become one of the key models that helped advance how machines see and recognize objects in photos. At its core, VGGNet is designed to look at images and decide what is in them.

LeNet-5 is an early and very influential type of convolutional neural network (CNN) developed by Yann LeCun and his colleagues in 1998, designed mainly to recognize handwritten digits like those in the MNIST dataset. What makes LeNet-5 special is how it combines several clever ideas that allow it to efficiently and accurately understand images despite their complexity—ideas that were crucial stepping stones for today’s deep learning revolution.

John Larry Kelly Jr. (December 26, 1923 – March 18, 1965), was an American scientist who worked at Bell Labs. From a system he’d developed to analyze information transmitted over networks, he created the Kelly Criterion, a formula that predicts the best way to bet or invest money. He was also a pioneer in the field of computer science and artificial intelligence.

Muon is a second-order optimizer for deep learning models, designed to accelerate training and reduce memory usage. It leverages information about the curvature of the loss landscape to achieve faster convergence and more efficient memory utilization. By overcoming historical computational barriers and standardizing its usage, Muon brings the theoretical advantages of second-order optimization to the scale required for LLMs, potentially reshaping both practice and expectations in deep learning.

At its core, accelerationism proposes that intensifying or pushing to extremes the processes inherent to modern capitalism and technology can destabilize existing social and political orders, potentially leading to the collapse or transformation of the status quo. This can create opportunities for something fundamentally new to emerge.