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Titlebook: Introduction to Deep Learning Business Applications for Developers; From Conversational Armando Vieira,Bernardete Ribeiro Book 2018 Arman

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發(fā)表于 2025-3-21 17:41:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Introduction to Deep Learning Business Applications for Developers
副標(biāo)題From Conversational
編輯Armando Vieira,Bernardete Ribeiro
視頻videohttp://file.papertrans.cn/474/473602/473602.mp4
概述Covers the latest developments in Deep Learning and offers concrete advice on how to implement Deep Learning in your business.Explains the complexities in deploying a Deep Learning platform – in-house
圖書(shū)封面Titlebook: Introduction to Deep Learning Business Applications for Developers; From Conversational  Armando Vieira,Bernardete Ribeiro Book 2018  Arman
描述Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles.?.An Introduction to Deep Learning Business Applications for Developers. covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. You’ll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer..After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of
出版日期Book 2018
關(guān)鍵詞Deep Learning; Deep Neural Networks; Natural Language Processing; Convolutional Neural Networks; Robotic
版次1
doihttps://doi.org/10.1007/978-1-4842-3453-2
isbn_softcover978-1-4842-3452-5
isbn_ebook978-1-4842-3453-2
copyright Armando Vieira, Bernardete Ribeiro 2018
The information of publication is updating

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Deep Learning: An Overview words, data without labels. They were called . (DBNs) and consisted of staked restrictive Boltzmann machines (RBMs), with each one placed on the top of another. DBNs differ from previous networks since they are generative models capable of learning the statistical properties of data being presented without any supervision.
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omplexities in deploying a Deep Learning platform – in-houseDiscover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotat
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New Research and Future Directionsthe near future, both supervised learning RNNs and reinforcement learning will be greatly scaled up. Current large ANNs have on the order of a billion connections; soon that will be a trillion, at the same price. By comparison, human brains have a trillions of—much slower—connections.
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IntroductionThis chapter will describe what the book is about, the book’s goals and audience, why artificial intelligence (AI) is important, and how the topic will be tackled.
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