This article discusses the importance of learning and inference in machine learning and edge AI. It explains how edge devices are more suited for…
Browsing: Backpropagation
Deep neural networks (DNNs) are powerful models that use linear algebra and activation functions to process input data and learn from it through the…
This article discusses the difference between backpropagation and prospective configuration, two methods of adjusting neural networks to better predict outcomes. Backpropagation adjusts the weights…
Loss functions are an essential component of deep learning, used to evaluate how well a specific algorithm models the given data. The goal of…
The 3rd day of the Google Machine Learning Community Summit was a dynamic and interactive experience, focusing on hands-on demonstrations by Google Developer Experts…
This article traces the evolution of machine learning from its roots in cybernetics in the mid-20th century to the modern day. It explores the…
Machine learning is a subset of artificial intelligence (AI) which grants computers the power to analyze large volumes of data, recognize patterns, and make…
This article provides a comprehensive glossary of AI terms and concepts, from artificial intelligence to machine learning and data mining. It explains each term…
This article provides an overview of key terms related to Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), Natural Language Understanding (NLU), and…
This project implements a completely functional engine for tracking operations between Tensors, by dynamically building a Directed Acyclic Graph (DAG), and an automatic backpropagation…