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Deep Learning for Human Activity Recognition

This book constitutes refereed proceedings of the Second International Workshop on Deep Learning for Human Activity Recognition, DL-HAR;

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Emerging Deep Learning Applications of Short Range Radars

interface (HMI) applications, such as gesture recognition and sensing, human activity classification, air-writing, material classification, vital;

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Advances in Computational Intelligence

forecasting; computational intelligence methods for time series; human activity recognition; new and future tendencies in brain-computer interface;

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Advances in Computational Intelligence

forecasting; computational intelligence methods for time series; human activity recognition; new and future tendencies in brain-computer interface;

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Human Activity Recognition

applications of human activity recognition (HAR). The authors examine how machine learning and pattern recognition tools help determine a user's;

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Deep Learning in Computer Vision

, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision;

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Deep Learning in Computer Vision: Principles and Applications

, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision;

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Human Activity Recognition Challenge

The book introduces some challenging methods and solutions to solve the human activity recognition challenge. This book highlights the;

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Deep Learning for NLP and Speech Recognition

Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech;

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Deep Learning for Nlp and Speech Recognition

Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech;

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Smartphone-Based Human Activity Recognition

The book reports on the author's original work to address the use of today's state-of-the-art smartphones for human physical activity;

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MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition

, researchers have begun applying deep learning strategies to image analysis and pattern recognition for solving technical issues within image;

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MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition

, researchers have begun applying deep learning strategies to image analysis and pattern recognition for solving technical issues within image;

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Deep Learning for Biometrics

, and provides comparison with popular CNN-based architectures for face recognition; examines deep learning for state-of-the-art latent;

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Machine Learning for Speaker Recognition

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition;

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Deep Learning with R

Description Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image;

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Deep Learning with Python

recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python;

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Automatic Speech Recognition: A Deep Learning Approach

learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the;

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Image Analysis and Recognition

; Signal Processing Techniques for Ultrasound Tissue Characterization and Imaging in Complex Biological Media; Advances in Deep Learning; Deep;

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Deep Learning with Applications Using Python

Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and;

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Artificial Intelligence. IJCAI 2019 International Workshops

Automated Negotiations. First International Workshop on Deep Learning for Human Activity Recognition. HAI 2019: Second International Workshop on;

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The Deep Learning Workshop

Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition;

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Deep Learning for Data Analytics

of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications;

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Deep Learning

speech recognition (ASR) and human action recognition.;

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Deep Learning

detection * Experiment with deep learning concepts, algorithms, and the toolbox for deep learning * Select and split data sets into training, test;

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Challenges and Trends in Multimodal Fall Detection for Healthcare

statistics for automated, human fall recognition systems and related topics using data fusion. It includes theory and coding implementations to help;

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Engineering Applications of Neural Networks

and selected from 39 submissions. The papers are organized in topical sections on activity recognition, deep learning, extreme learning;

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