Neural Architecture

Neural Architecture
Author: Matias del Campo
Publisher: Applied Research & Design
Total Pages: 250
Release: 2022-03-14
Genre:
ISBN: 9781951541682

This book explores the interdisciplinary project that brings the long tradition of humanistic inquiry in architecture together with cutting-edge research in artificial intelligence. The main goal of Neural Architecture is to understand how to interrogate artificial intelligence - a technological tool - in the field of architectural design, traditionally a practice that combines humanities and visual arts. Matias del Campo, the author of Neural Architecture is currently exploring specific applications of artificial intelligence in contemporary architecture, focusing on their relationship to material and symbolic culture. AI has experienced an explosive growth in recent years in a range of fields including architecture but its implications for the humanistic values that distinguish architecture from technology have yet to be measured. The book illustrates in a series of projects a set of crucial questions for the development of architecture in the future. An opportunity to survey the emerging field of Architecture and Artificial Intelligence, and to reflect on the implications of a world increasingly entangled in questions of the agency, culture and ethics of AI.

Automated Machine Learning

Automated Machine Learning
Author: Frank Hutter
Publisher: Springer
Total Pages: 223
Release: 2019-05-17
Genre: Computers
ISBN: 3030053180

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Introduction to Neural Architecture Search

Introduction to Neural Architecture Search
Author: Robert Johnson
Publisher: HiTeX Press
Total Pages: 245
Release: 2024-10-27
Genre: Computers
ISBN:

"Introduction to Neural Architecture Search: Optimizing AI Models" delves into the transformative realm of automating neural network design. As the AI landscape advances rapidly, NAS has emerged as an essential field, streamlining the creation of efficient, high-performance models. This book offers a comprehensive examination of NAS's foundational concepts, cutting-edge algorithms, and real-world applications, making it an indispensable resource for those seeking to deepen their understanding of AI model optimization. Designed to cater to a diverse audience, from beginners to seasoned practitioners, the book meticulously explores each facet of NAS, from the underlying neural network principles to intricate evaluation methods. Readers will gain insights into popular NAS algorithms, tools, and frameworks, complemented by case studies illuminating NAS's practical impact. As it addresses current challenges and future directions, the book empowers readers to navigate the evolving landscape of NAS, equipping them with the knowledge needed to spearhead innovative AI solutions.

SpiNNaker - A Spiking Neural Network Architecture

SpiNNaker - A Spiking Neural Network Architecture
Author: Steve Furber
Publisher: NowOpen
Total Pages: 352
Release: 2020-03-15
Genre:
ISBN: 9781680836523

This books tells the story of the origins of the world's largest neuromorphic computing platform, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over

How to Build a Brain

How to Build a Brain
Author: Chris Eliasmith
Publisher: Oxford University Press
Total Pages: 475
Release: 2013-04-16
Genre: Psychology
ISBN: 0199794693

How to Build a Brain provides a detailed exploration of a new cognitive architecture - the Semantic Pointer Architecture - that takes biological detail seriously, while addressing cognitive phenomena. Topics ranging from semantics and syntax, to neural coding and spike-timing-dependent plasticity are integrated to develop the world's largest functional brain model.

Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances

Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances
Author: Yanan Sun
Publisher: Springer Nature
Total Pages: 335
Release: 2022-11-08
Genre: Technology & Engineering
ISBN: 3031168682

This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.

Deep Learning Architectures

Deep Learning Architectures
Author: Ovidiu Calin
Publisher: Springer Nature
Total Pages: 760
Release: 2020-02-13
Genre: Mathematics
ISBN: 3030367215

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.

Artificial Neural Networks in Real-life Applications

Artificial Neural Networks in Real-life Applications
Author: Juan Ramon Rabunal
Publisher: IGI Global
Total Pages: 395
Release: 2006-01-01
Genre: Technology & Engineering
ISBN: 1591409020

"This book offers an outlook of the most recent works at the field of the Artificial Neural Networks (ANN), including theoretical developments and applications of systems using intelligent characteristics for adaptability"--Provided by publisher.

Neural Network Design

Neural Network Design
Author: Martin T. Hagan
Publisher:
Total Pages:
Release: 2003
Genre: Neural networks (Computer science)
ISBN: 9789812403766