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Efficient Processing of Deep Neural Networks

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks;

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Efficient Processing of Deep Neural Networks

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks;

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Tree-Based Convolutional Neural Networks

This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data;

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Neural Information Processing

The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing;

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Neural Information Processing Part I

Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and;

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Neural Information Processing Part II

Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and;

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Neural Information Processing Part III

Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and;

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Neural Information Processing Part IV

Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and;

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Deep Neural Networks and Applications

Deep Neural Networks and Applications makes the readers aware about the various artificial neural networks (ANN) and the topologies related;

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Hardware Architectures for Deep Learning

, and IoT solutions. Efficient implementation of neural networks to support complex deep learning-based applications is a complex challenge for;

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Deep Learning with Keras

Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book * Implement various deep;

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Deep Learning with Hadoop

the challenges of implementing distributed deep learning with Hadoop and how to overcome it * Implement Convolutional Neural Network (CNN;

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Hands-On Deep Learning for Images with TensorFlow

Explore TensorFlow's capabilities to perform efficient deep learning on images Key Features Discover image processing for machine vision;

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Neural Information Processing

The three-volume set of LNCS 11953, 11954, and 11955 constitutes the proceedings of the 26th International Conference on Neural Information;

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Advanced Deep Learning with Python

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural;

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Trends in Deep Learning Methodologies

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep;

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Handbook on Neural Information Processing

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical;

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Deep Learning for Natural Language Processing

Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key Features Gain;

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Neural Information Processing

Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019.For ICONIP 2019 a total of 345 papers was carefully;

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Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools

decision tools can make deep neural networks more interpretable - and even, in many cases, more efficient. Fuzzy logic together with multi;

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TensorFlow Deep Learning Projects

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios Key Features Build efficient deep;

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Advances in Big Data

The book offers a timely snapshot of neural network technologies as a significant component of big data analytics platforms. It promotes;

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PyTorch Deep Learning Hands-On

major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and;

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Deep Learning Essentials

the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural;

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Neural Networks for Natural Language Processing

the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative;

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Neural Networks for Natural Language Processing

the practical implementations of this trending approach. Neural Networks for Natural Language Processing is a collection of innovative;

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Artificial Neural Networks and Machine Learning - ICANN 2019: Image Processing

Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented;

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