Browsing: Gradient Descent

The continual backpropagation algorithm extends standard deep learning by adding a source of continuing variability to the weights of the network, allowing for the…

This article discusses the challenges of using deep learning in autonomous vehicles, specifically in regards to the problem of imbalanced data. The authors propose…

This article discusses the need for advanced optimization techniques in machine learning, as traditional methods are not sufficient for navigating the complexities of modern…

Loss functions are an essential component of deep learning, used to evaluate how well a specific algorithm models the given data. The goal of…

This post by Lorenz Kuger reflects on the recent success of machine learning models and the associated challenges. To prevent these problems, Kuger introduces…

This book introduces deep learning from the bottom up, using a Socratic approach and humorous writing style. It leads readers through the complete implementation…