Online-Ressource
Verfasst von:	Chin, Ee Kin [VerfasserIn]
Titel:	The deep learning architect's handbook
Titelzusatz:	build and deploy production-ready DL solutions leveraging the latest Python techniques
Verf.angabe:	Ee Kin Chin
Ausgabe:	1st edition.
Verlagsort:	Birmingham
Verlag:	Packt Publishing
Jahr:	2023
Umfang:	1 online resource (530 pages)
Fussnoten:	Includes index
ISBN:	978-1-80323-534-9
	1-80323-534-9
	978-1-80324-379-5
Abstract:	Deep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.
URL:	Aggregator: https://learning.oreilly.com/library/view/-/9781803243795/?ar
Datenträger:	Online-Ressource
Sprache:	eng
Bibliogr. Hinweis:	Erscheint auch als : Druck-Ausgabe
Sach-SW:	Apprentissage profond
	Apprentissage profond ; Applications industrielles
K10plus-PPN:	1879406128
	Universitätsbibliothek
Lokale URL UB:	Zum Volltext
	Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
	Bestellen/Vormerken für Benutzer des Klinikums Mannheim Eigene Kennung erforderlich
Bibliothek/Idn:	UW / m4471886975
Lokale URL Inst.:	Zum Volltext