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Recurrent Neural Networks for Temporal Data Processing

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Recurrent Neural Networks for Prediction

Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and;

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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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Supervised Sequence Labelling with Recurrent Neural Networks

, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and;

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Hands-on Machine Learning with Python

introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine;

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Neural Information Processing

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

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Hands-on Machine Learning with Python

sequences in recurrent neural networks and long short term memory Who This Book Is For Data scientists, machine learning engineers, and;

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data;

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data;

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Neural Networks for Applied Sciences and Engineering

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Recurrent Neural Networks; Neuromorphic Hardware; Brain Topology and Dynamics; Neural Networks Meet Natural and Environmental Sciences;

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track

autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent;

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track

autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent;

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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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Machine Learning and Knowledge Discovery in Databases

autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent;

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Machine Learning and Knowledge Discovery in Databases

autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent;

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Machine Learning and Knowledge Discovery in Databases

autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent;

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Deep Learning from Scratch Building with Python from First Principles

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many;

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PyTorch Recipes

algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you;

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Advanced Deep Learning with Python

LSTM and GRU. You'll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs;

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Deep Learning for Natural Language Processing

Explore convolutional and recurrent neural networks and long short-term memory networks Book DescriptionApplying deep learning approaches to;

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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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Deep Learning with Keras

in special cases * Identify problems for which Recurrent Neural Network (RNN) solutions are suitable * Explore the process required to implement;

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Sensory Neural Networks

behavior, their relation to recent activity in neural networks and connectionist systems, their use for image processing applications, and their;

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Revival: Sensory Neural Networks (1991)

behavior, their relation to recent activity in neural networks and connectionist systems, their use for image processing applications, and their;

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Artificial Neural Networks with Java

Use Java for the development of neural network applications Prepare data for many different tasks Carry out some unusual neural network;

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