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Supervised Sequence Labelling with Recurrent Neural Networks

transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the;

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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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Hands-on Machine Learning with Python

* Examine convolutional neural networks and Recurrent neural networks * Get acquainted with scikit-learn and PyTorch * Predict sequences;

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Handbook on Neural Information Processing

applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks;

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Hands-on Machine Learning with Python

unsupervised learning Examine convolutional neural networks and Recurrent neural networks Get acquainted with scikit-learn and PyTorch Predict;

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Natural Language Processing

unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the;

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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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PyTorch Recipes

and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor;

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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 with Keras

you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this;

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Blind Equalization in Neural Networks

The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms;

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Advanced Deep Learning with Python

train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs;

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Deep Learning from Scratch Building with Python from First Principles

neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural;

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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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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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Neural Network Programming with Java

implement these in Java. Recurrent neural networks, which are believed to have memory, are discussed in detail. These work in such a way that the;

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Recurrent Neural Networks for Temporal Data Processing

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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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Supervised and Unsupervised Pattern Recognition

research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy;

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Neural Networks with Keras Cookbook

about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for;

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Simulating Neural Networks with Mathematica

with Neural Networks Feedback and Recurrent Networks Adaptive Resonance Theory Genetic Algorithms 020156629XB04062001;

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Neural Information Processing

networks, recurrent neural networks, and spiking neural networks.;

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Advances in Neural Networks - ISNN 2016

cover many topics of neural network-related research including signal and image processing; dynamical behaviors of recurrent neural networks;

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Deep Learning in Mining of Visual Content

some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks. Deep Learning;

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Engineering Applications of Neural Networks

machine, machine learning applications, predictive models, fuzzy and recommender systems, recurrent neural networks, spiking neural networks.;

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Recurrent Neural Networks for Prediction

presenting the latest research work the authors demonstrate how real--time recurrent neural networks (RNNs) can be implemented to expand the range;

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