Packt Publishing, 2019. — 269 p. — ISBN: 978-1-78899-259-6. Code files only! Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis,...
Packt Publishing Ltd., 2019. — 301 p. — ISBN: 978-1-78913-890-0. Code files only! Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical...
Packt, 2019. — 368 p. — ISBN: 9781838824914. !Code files ncrease the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and Deep Neuroevolution. Key Features Implement neuroevolution algorithms to improve performance of various neural network architectures Get well-versed with evolutionary algorithms and neuroevolution...
Packt, 2019. — 462 p. — ISBN: 9781789536089. !Code files Book Description Your one-stop guide to learning and implementing artificial neural networks with Keras effectively Key Features Design and create neural network architectures on different domains using Keras Integrate neural network models in your applications using this highly practical guide Get ready for the future of...
Packt Publishing, 2018 - 272p. - ISBN: 1789130336 !Code files only! Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features Learn the fundamentals of Convolutional Neural Networks Harness...
М.: ФИЗМАТЛИТ, 2001. — 201 с. В книге рассмотрены теоретические аспекты подобных сетей,именно, аппарат нечеткой логики,основы теории искусственных нейронных сетей и собственно гибридных сетей применительно к задачам управлениям и принятия решения в условиях неопределенности. Рекомендовано УМО по образованию в области статистики, прикладной информатики и математических методов в...
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