Da: Best Price, Torrance, CA, U.S.A.
EUR 40,60
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EUR 46,66
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 45,44
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Editore: Packt Publishing 12/12/2018, 2018
ISBN 10: 1789957214 ISBN 13: 9781789957211
Lingua: Inglese
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
EUR 50,16
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Python Advanced Guide to Artificial Intelligence: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents us 2.84. Book.
EUR 54,05
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Da: Chiron Media, Wallingford, Regno Unito
EUR 50,84
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 56,44
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 53,85
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 61,42
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Da: La Casa de los Libros, Castellgali, BARCE, Spagna
EUR 32,19
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Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 90,72
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Aggiungi al carrelloPaperback. Condizione: New. New. book.
Da: PBShop.store US, Wood Dale, IL, U.S.A.
EUR 62,31
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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 55,07
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Aggiungi al carrelloPAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: Majestic Books, Hounslow, Regno Unito
EUR 70,17
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 764.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 70,84
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features:Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and more Build, deploy, and scale end-to-end deep neural network models in a production environment Book Description: This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: ¿ Mastering Machine Learning Algorithms by Giuseppe Bonaccorso ¿ Mastering TensorFlow 1.x by Armando Fandango ¿ Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn:Explore how an ML model can be trained, optimized, and evaluated Work with Autoencoders and Generative Adversarial Networks Explore the most important Reinforcement Learning techniques Build end-to-end deep learning (CNN, RNN, and Autoencoders) models Who this book is for: This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.