Geo.Engineerings

Pramod Singh, Avinash Manure ... 164 pages - Publisher: Apress; (December, 2019) ... Language: English - ASIN: B082X9CM42 by Amazon.

Sandro Skansi ... 140 pages - Publisher: Springer; (January, 2020) ... Language: English - ASIN: B0846GYCDD by Amazon.

This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science. The text also highlights select topics from the fascinating history of this exciting field, including the pioneering work of Rudolf Carnap, Warren McCulloch, Walter Pitts, Bulcsú László, and Geoffrey Hinton.

Topics and features: Provides a brief history of mathematical logic, and discusses the critical role of philosophy, psychology, and neuroscience in the history of AI + Presents a philosophical case for the use of fuzzy logic approaches in AI + Investigates the similarities and differences between the Word2vec word embedding algorithm, and the ideas of Wittgenstein and Firth on linguistics + Examines how developments in machine learning provide insights into the philosophical challenge of justifying inductive inferences + Debates, with reference to philosophical anthropology, whether an advanced general artificial intelligence might be considered as a living being + Investigates the issue of computational complexity through deep-learning strategies for understanding AI-complete problems and developing strong AI + Explores philosophical questions at the intersection of AI and transhumanism. This inspirational volume will rekindle a passion for deep learning in those already experienced in coding and studying this discipline, and provide a philosophical big-picture perspective for those new to the field.

M. Arif Wani, Farooq Ahmad Bhat, Saduf Afzal, Asif Iqbal Khan ... 149 pages - Publisher: Springer; (March, 2019) ... Language: English - ASIN: B07PN2QZKM by Amazon.

This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.