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Keras

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Packt Publishing, 2019. — 316 р. — ISBN 978-1789136678. Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training...
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Packt Publishing, 2018. — 368 p. — ASIN B078N8RDCP. A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks, Autoencoders, GANs, VAEs, and...
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Packt Publishing, 2018. — 368 p. — ASIN B078N8RDCP. A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks, Autoencoders, GANs, VAEs, and...
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Packt Publishing, 2018. — 368 p. — ASIN B078N8RDCP. A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks, Autoencoders, GANs, VAEs, and...
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Packt Publishing, 2018. — 368 p. — ASIN B078N8RDCP. !Code files only. A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks, Autoencoders,...
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Packt Publishing, 2019. — 558 p. — ISBN 1789346649. This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data. Later,...
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Packt Publishing, 2019. — 558 p. — ISBN 1789346649. !Code files only This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and...
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Packt, 2018. — 386 p. — ISBN 1789536642. Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects,...
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Packt, 2018. — 386 p. — ISBN 1789536642. !Code files only. Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through...
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Packt, 2018. — 288 p. Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library....
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Packt Publishing, 2017. — 318 p. — ISBN 978-1-78712-842-2. Get to grips with the basics of Keras to implement fast and efficient deep-learning models This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing...
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Packt Publishing, 2017. — 318 p. — ISBN 978-1-78712-842-2. Get to grips with the basics of Keras to implement fast and efficient deep-learning models This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing...
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Packt Publishing, 2017. — 318 p. — ISBN 978-1-78712-842-2. True PDF Get to grips with the basics of Keras to implement fast and efficient deep-learning models This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image...
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Packt Publishing, 2017. — 318 p. — ISBN 978-1-78712-842-2. Get to grips with the basics of Keras to implement fast and efficient deep-learning models This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing...
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Amazon Digital Services LLC, 2018. — 189 р. This introduction will help you develop a good understanding of deep learning completely from scratch This book covers: Introduction to machine learning and deep learning Math for deep learning explained to the layman How neural networks work: a general overview Activation functions in deep networks Loss functions Weight initialization...
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Amazon Digital Services LLC, 2018. — 189 р. This introduction will help you develop a good understanding of deep learning completely from scratch This book covers: Introduction to machine learning and deep learning Math for deep learning explained to the layman How neural networks work: a general overview Activation functions in deep networks Loss functions Weight initialization...
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Amazon Digital Services LLC, 2018. — 189 р. This introduction will help you develop a good understanding of deep learning completely from scratch This book covers: Introduction to machine learning and deep learning Math for deep learning explained to the layman How neural networks work: a general overview Activation functions in deep networks Loss functions Weight initialization...
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Apress, 2019. — 182 p. Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two...
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Apress, 2019. — 182 p. Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two...
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Apress, 2019. — 182 p. — ISBN-13: 978-1-4842-4239-1. Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book...
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Apress, 2019. — 182 p. — ISBN-13 (electronic): 978-1-4842-4240-7. Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The...
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Apress, 2019. — 182 p. — ISBN-13 (electronic): 978-1-4842-4240-7. Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The...
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Wiley, 2019. — 313 p. — ISBN 978-1-119-56486-7. Build a Keras model to scale and deploy on a Kubernetes cluster We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within AI, we’re seeing a particular growth...
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М.: ДМК-Пресс, 2017. — 294 с. — ISBN 978-5-97060-573-8. Книга представляет собой краткое, но обстоятельное введение в современные нейронные сети, искусственный интеллект и технологии глубокого обучения. В ней представлено более 20 работоспособных нейронных сетей, написанных на языке Python с использованием модульной библиотеки Keras, работающей поверх библиотек TensorFlow от...
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