
Artificial Neural Networks
These are lecture notes for my course on Artificial Neural Networks that...
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Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot
The article substantiates the necessity to develop training methods of c...
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A Comprehensive Survey on Bengali Phoneme Recognition
Hidden Markov model based various phoneme recognition methods for Bengal...
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Wireless Networks Design in the Era of Deep Learning: ModelBased, AIBased, or Both?
This work deals with the use of emerging deep learning techniques in fut...
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Classification by Ensembles of Neural Networks
We introduce a new procedure for training of artificial neural networks ...
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Adaptive Wind Driven Optimization Trained Artificial Neural Networks
This paper presents the application of a newly developed natureinspired...
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Engineers Code: reusable open learning modules for engineering computations
Undergraduate programs in science and engineering include at least one c...
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CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"
The role of neural network modeling in the learning content of the special course "Foundations of Mathematical Informatics" was discussed. The course was developed for the students of technical universities  future ITspecialists and directed to breaking the gap between theoretic computer science and it's applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic "Neural network and pattern recognition" of the special course "Foundations of Mathematic Informatics" are shown. The program code was presented in a CoffeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network's weights, etc. The features of the KolmogorovArnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a threelayer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.
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