Titlebook: Deep Learning Classifiers with Memristive Networks; Theory and Applicati Alex Pappachen James Book 2020 Springer Nature Switzerland AG 2020

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Overview of Long Short-Term Memory Neural Networksus modifications of an original LSTM cell were proposed. This chapter gives an overview of basic LSTM cell structures and demonstrates forward and backward propagation within the most widely used configuration called traditional LSTM cell. Besides, LSTM neural network configurations are described.

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Introduction to Neuro-Memristive Systemsogies has been largely attributed to the convergence in the growth on computational capabilities, and that of the large availability of the data resulting from Internet of things applications. The need to have higher computational capabilities enforces the need to have low power solutions and smalle

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Memristors: Properties, Models, Materialstics. This chapter covers the basics of memristor characteristics, models and a succinct review of practically realized memristive devices. Memristors represent a class of two terminal resistive switching multi-state memory devices that can be compatible with existing integrated circuit technologies

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Getting Started with TensorFlow Deep Learningto its simplicity, flexibility, and compatibility. In this chapter, we introduce the basic syntax of the TensorFlow and its main operations required to construct an artificial neural network. We briefly introduce the codes for building a recurrent neural network and convolutional neural network for

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Deep-Learning-Based Approach for Outdoor Electrical Insulator Inspectionpport to conductors. Overhead insulators need to be inspected and monitored regularly to prevent faults and provide permanent electricity for consumers. The condition monitoring system for insulators is quite a challenging task due to the harsh operating conditions and the large number of insulators