Deep Learning

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.

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.

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.

The journey of pioneers in Machine Learning (ML) and Artificial Intelligence (AI) is a remarkable tale of innovation, collaboration, and the relentless pursuit of knowledge.

Gran Turismo Sophy is an advanced AI racing agent developed through a collaboration between Sony AI, Polyphony Digital, and Sony Interactive Entertainment. This groundbreaking technology utilizes deep reinforcement learning to master the complexities of competitive racing in the Gran Turismo Sport simulator. Initially starting as an AI that struggled to navigate tracks, Sophy has evolved into a formidable competitor capable of challenging top human drivers by mastering racing tactics, etiquette, and vehicle control.

In 2012, the field of artificial intelligence witnessed a seismic shift. The catalyst for this transformation was a deep learning model known as AlexNet.