Advances In Deep Learning

Advances in Deep Learning
Publisher Springer
Release Date
Category Technology & Engineering
Total Pages 149
ISBN 9789811367946
Rating 1/5 from 1 reviews
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This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.

Advances in Deep Learning
  • Author : M. Arif Wani,Farooq Ahmad Bhat,Saduf Afzal,Asif Iqbal Khan
  • Publisher : Springer
  • Release Date : 2019-03-14

This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by

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Advances in Machine Learning Deep Learning based Technologies
  • Author : George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain
  • Publisher : Springer
  • Release Date : 2021-10-16

As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities

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Advances in Financial Machine Learning
  • Author : Marcos Lopez de Prado
  • Publisher : John Wiley & Sons
  • Release Date : 2018-01-23

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will

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Recent Advances in Big Data and Deep Learning
  • Author : Luca Oneto,Nicolò Navarin,Alessandro Sperduti,Davide Anguita
  • Publisher : Springer
  • Release Date : 2019-04-02

This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the

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Machine Learning Paradigms
  • Author : George A. Tsihrintzis,Lakhmi C. Jain
  • Publisher : Springer
  • Release Date : 2021-07-25

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some

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Machine Learning Paradigms
  • Author : George A. Tsihrintzis,Lakhmi C. Jain
  • Publisher : Springer Nature
  • Release Date : 2020-08-24

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some

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Advances in Machine Learning Deep Learning based Technologies
  • Author : George A. Tsihrintzis
  • Publisher : Springer Nature
  • Release Date : 2021-09-16

Read online Advances in Machine Learning Deep Learning based Technologies written by George A. Tsihrintzis, published by Springer Nature which was released on . Download full Advances in Machine Learning Deep Learning based Technologies Books now! Available in PDF, ePub and Kindle.

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Artificial Intelligence
  • Author : Marco Antonio Aceves-Fernandez
  • Publisher : BoD – Books on Demand
  • Release Date : 2018-06-27

Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict

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Advances in Machine Learning and Computational Intelligence
  • Author : Srikanta Patnaik,Xin-She Yang,Ishwar K. Sethi
  • Publisher : Springer Nature
  • Release Date : 2020-07-25

This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence (ICMLCI-2019), jointly organized by Kunming University of Science and Technology and the Interscience Research Network, Bhubaneswar, India, from April 6 to 7, 2019. Addressing virtually all aspects of intelligent systems, soft computing and machine learning, the

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Advances in Deep Learning
  • Author : M. Arif Wani,Farooq Ahmad Bhat,Saduf Afzal,Asif Iqbal Khan
  • Publisher : Springer
  • Release Date : 2019-05-24

This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by

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Hyperspectral Image Analysis
  • Author : Saurabh Prasad,Jocelyn Chanussot
  • Publisher : Springer Nature
  • Release Date : 2020-04-27

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold

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Deep Learning
  • Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
  • Publisher : MIT Press
  • Release Date : 2016-11-10

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO

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Advances in Financial Machine Learning
  • Author : Marcos Lopez de Prado
  • Publisher : John Wiley & Sons
  • Release Date : 2018-02-21

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will

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Advances and Applications in Deep Learning
  • Author : Anonim
  • Publisher : BoD – Books on Demand
  • Release Date : 2020-12-09

Artificial Intelligence (AI) has attracted the attention of researchers and users alike and is taking an increasingly crucial role in our modern society. From cars, smartphones, and airplanes to medical equipment, consumer applications, and industrial machines, the impact of AI is notoriously changing the world we live in. In this

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Advances in Machine Learning and Data Mining for Astronomy
  • Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
  • Publisher : CRC Press
  • Release Date : 2012-03-29

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

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