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Supervised Learning with Complex-valued Neural Networks

monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The;

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Complex-valued Neural Networks

Recent research indicates that complex-valued neural networks whose parameters (weights and threshold values) are all complex numbers are;

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Complex-Valued Neural Networks

This book covers the hot topics and applications surrounding complex-valued neural networks. It demonstrates advanced theories with a wide;

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Complex-valued Neural Networks

In recent years, complex-valued neural networks have widened the scope of application in optoelectronics, imaging, remote sensing, quantum;

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Complex-Valued Neural Networks with Multi-Valued Neurons

Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book;

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Hands-on Machine Learning with Python

third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks;

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TensorFlow 2.0 Quick Start Guide

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features Train;

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Hands-on Machine Learning with Python

third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks;

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Neural Networks with R

in neural networks with R, to implement them in your applications * Build smart systems using the power of deep learning * Real-world case studies;

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Advances in Neural Networks ISNN 2020

were organized in topical sections named: optimization algorithms; neurodynamics, complex systems, and chaos; supervised/unsupervised;

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Complex-Valued Neural Networks

that deal with complex numbers? What advantages do the complex-valued neural networks have? What is the origin of the advantages? In what areas;

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Mastering Machine Learning Algorithms

data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement;

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Handbook on Neural Information Processing

Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple;

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PyTorch Recipes

Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch;

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Neural Network Methods in Natural Language Processing

. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics;

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Neural Network Methods in Natural Language Processing

. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics;

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Deep Learning for Data Analytics

, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networksProvides readers with an introduction to Deep;

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Recurrent Neural Networks

scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Learn Keras for Deep Neural Networks

solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification;

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MATLAB Deep Learning

learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with;

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Neural Network Learning

This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised;

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Neural Network Learning

This book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised;

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Fundamentals Of Deep Learning

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that's paving;

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Focus on Artificial Neural Networks

of pharamceutical microemulsions; massive-training artificial neural networks for supervised enhancement/suppression of lesions/patterns in medical images;

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Deep Learning and Practice with MindSpore

AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;

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