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008			230114s2022 si | s |1|| 0|eng d
020			_a9789811633577
024	7		_a10.1007/978-981-16-3357-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
245	0	0	_aDeep Learning Applications, Volume 3 _cedited by M. Arif Wani, Bhiksha Raj, Feng Luo, Dejing Dou
250			_a1st edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XII, 322 páginas) _b113 ilustraciones, 96 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aAdvances in Intelligent Systems and Computing _x2194-5365 _v1395
505	0		_aDeep Rapid Class Augmentation; a New Progressive Learning Approach that Eliminates the Issue of Catastrophic Forgetting -- A Comprehensive Analysis of Subword Contextual Embeddings for Languages with Rich Morphology -- RGB and Depth Image Fusion for Object Detection using Deep Learning -- Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis -- A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks -- Deep Learning based Time Series Forecasting -- DEAL: Deep Evidential Active Learning for Image Classification -- LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi[1]class Classification -- Efficient Deployment of Deep Learning Models on Autonomous Robots in the ROS Environment -- Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience -- Improving the Donor Journey with Convolutional and Recurrent Neural Networks.
520			_aThis book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers. .
988			_aSpringer_Robotics_2022
650		7	_2embne _9166090 _aAprendizaje automático _vCongresos y asambleas
776	0	8	_iPrinted edition: _z9789811633560
776	0	8	_iPrinted edition: _z9789811633584
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-3357-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eIG _zSI