Deep learning research applications (130 résultats)

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paperback. 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.…

paperback. 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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Etat : 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.

Taschenbuch. Etat : Neu. Neuware - 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 the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance.Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.…

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Buch. 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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