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Langue: anglais
Edité par China Agriculture Press, 2025
ISBN 10 : 7109331237 ISBN 13 : 9787109331235
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Ajouter au panierpaperback. Etat : New. Language:Chinese.Paperback. Pub Date: 2025-02 Pages: 188 Publisher: China Agriculture Press Deep learning technology is a cutting-edge technology in the fields of computer science and artificial intelligence. providing crucial technical support for the development of smart agriculture and representing an important technological direction for my country's agricultural modernization. rural digitalization. and rural revitalization. This book systematically introduces the basic concepts. historic.
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Edité par China Economic Publishing House, 2024
ISBN 10 : 7513677735 ISBN 13 : 9787513677738
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Ajouter au panierpaperback. Etat : New. Language:Chinese.Paperback. Pub Date: 2024-05 Pages: 164 Publisher: China Economic Publishing House Quantitative investment. as an innovative form of financial technology. utilizes quantitative methods such as mathematics. statistics. and computer science to formulate investment strategies and make decisions. Its main goal is to find predictable patterns and trends by analyzing large amounts of market data and historical price trends. thereby optimizing portfolios. reducing risk. and increasi.
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Ajouter au panierEtat : New. The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents.
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.
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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.
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Ajouter au panierHardcover. Etat : Brand New. 256 pages. 6.69x9.44x0.55 inches. In Stock.
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Ajouter au panierHRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.