Deep learning (DL) has proven to be a highly effective approach for developing models in diverse contexts, including visual perception, speech recognition, and machine translation. Automated deep learning (AutoDL) endeavors to minimize the need for human involvement and is best known for its achievements in neural architecture search (NAS).

In this monograph, the authors examine research efforts into automation across the entirety of an archetypal DL workflow. In so doing, they propose a comprehensive set of ten criteria by which to assess existing work in both individual publications and broader research areas, namely novelty, solution quality, efficiency, stability, interpretability, reproducibility, engineering quality, scalability, generalizability, and eco-friendliness.

Aimed at students and researchers, this monograph provides an evaluative overview of AutoDL in the early 2020s, identifying where future opportunities for progress may exist.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.

Anbieterinformationen

We carry a wide selection of books from South Asia, United States, UK.

Zur Homepage des Verk�ufers

Unternehmensdaten des Verk�ufers

Readingos GmbH
Kaiserstra�e 47, Frankfurt am Main, 60329, Germany

Gesch�ftsbedingungen:

Versandinformationen:

To ensure faster delivery, books may be shipped from any of the following locations; Germany, the United Kingdom (UK), the United States (US),based on the buyer's address and product availability.

Impressum & Info zum Verk�ufer

Zahlungsarten
akzeptiert von diesem Verk�ufer

Zu Kaufgesuchen hinzuf�gen

Verwandte Artikel zu Automated Deep Learning: Neural Architecture Search...

Automated Deep Learning: Neural Architecture Search Is Not the End

Dong, Xuanyi; Kedziora, David Jacob; Musial, Katarzyna; Gabrys, Bogdan

Neu - Softcover

Unternehmensdaten des Verk�ufers