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Advanced Computing, Machine Learning, Robotics and Internet Technologies [[electronic resource] ] : First International Conference, AMRIT 2023, Silchar, India, March 10–11, 2023, Revised Selected Papers, Part I / / edited by Prodipto Das, Shahin Ara Begum, Rajkumar Buyya
| Advanced Computing, Machine Learning, Robotics and Internet Technologies [[electronic resource] ] : First International Conference, AMRIT 2023, Silchar, India, March 10–11, 2023, Revised Selected Papers, Part I / / edited by Prodipto Das, Shahin Ara Begum, Rajkumar Buyya |
| Autore | Das Prodipto |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (297 pages) |
| Disciplina | 004 |
| Altri autori (Persone) |
BegumShahin Ara
BuyyaRajkumar |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Machine learning Application software Artificial Intelligence Computer Engineering and Networks Machine Learning Computer Communication Networks Computer and Information Systems Applications |
| ISBN | 3-031-47224-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Hybrid Framework for implementing Modified K-Means Clustering Algorithm for Hindi Word Sense Disambiguation .-Detection of Leaf Disease using Mask Region based Convolutional Neural Network -- An Improved Machine Learning approach for throughput prediction in the Next Generation Wireless Networks -- Protein Secondary Structure Prediction without Alignment using Graph Neural Network -- A Lexicon-Based Approach for Sentiment Analysis of Bodo Language -- An Osprey Optimization based Efficient Controlling of Nuclear Energy-Based Power System -- Fuzzy Association Rule Mining Techniques and Applications -- Brain Tumor Detection Using VGG-16 -- Deep Learning model for Fish Copiousness Detection to Maintain the Ecological Balance between Marine Food Resources and Fishermen -- A Study on Smart Contract Security Vulnerabilities -- GUI Based Study of Weather Prediction using Machine Learning Algorithms -- A Systematic Review on Latest Approaches of Automated Sleep Staging System using Machine Intelligence Techniques -- Network Security Threats Detection Methods based on Machine Learning Techniques -- Optimized Traffic Management in Software Defined Networking -- Information Extraction for Design of a Multi-Feature Hybrid Approach for Pronominal Anaphora Resolution in a Low Resource Language -- Signature-based Batch Auditing Verification in Cloud Resource Pool -- Genetic Algorithm based Anomaly Detection for Intrusion Detection -- Machine learning based Malware Identification And Classification in PDF: A Review paper -- A Survey on Lung Cancer Detection and Location from CT Scan using Image Segmentation and CNN -- Bi-directional Long Short-Term Memory with Gated Recurrent Unit Approach for Next Word Prediction in Bodo Language -- Authorship Attribution for Assamese Language Documents: Initial Results -- Load Balancing and Energy Efficient Routingin Software-Defined Networking -- Sentiment Analysis: Indian Languages Perspective. . |
| Record Nr. | UNINA-9910847589803321 |
Das Prodipto
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced Concepts for Intelligent Vision Systems [[electronic resource] ] : 20th International Conference, ACIVS 2020, Auckland, New Zealand, February 10–14, 2020, Proceedings / / edited by Jacques Blanc-Talon, Patrice Delmas, Wilfried Philips, Dan Popescu, Paul Scheunders
| Advanced Concepts for Intelligent Vision Systems [[electronic resource] ] : 20th International Conference, ACIVS 2020, Auckland, New Zealand, February 10–14, 2020, Proceedings / / edited by Jacques Blanc-Talon, Patrice Delmas, Wilfried Philips, Dan Popescu, Paul Scheunders |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XV, 568 p. 305 illus., 231 illus. in color.) |
| Disciplina | 006.37 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Computer organization Application software Machine learning Education—Data processing Image Processing and Computer Vision Computer Systems Organization and Communication Networks Information Systems Applications (incl. Internet) Machine Learning Computers and Education Computer Appl. in Social and Behavioral Sciences |
| ISBN | 3-030-40605-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep Learning -- Design data model for Big Data Analysis System -- Deep Learning-based Techniques for Plant Diseases Recognition in Real-Field Scenarios -- EpNet: a Deep Neural Network for Ear Detection in 3D Point Clouds -- Fire Segmentation in Still Images -- Region Proposal Oriented Approach for Domain Adaptive Object Detection -- Deep Convolutional Network-Based Framework for Melanoma Lesion Detection and Segmentation -- A Novel Framework for Early Fire Detection Using Terrestrial and Aerial 360-degree Images -- Biomedical Image Analysis -- Segmentation of Phase-Contrast MR Images for Aortic Pulse Wave Velocity Measurements -- On the Uncertainty of Retinal Artery-vein Classification with Dense Fully-convolutional Neural Networks -- Object Contour Refinement using Instance Segmentation in Dental Images -- Correction of Temperature Estimated from a Low-Cost Handheld Infrared Camera for Clinical Monitoring -- Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic -- Deep-Learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography -- Quadratic Tensor Anisotropy Measures for Reliable Curvilinear Pattern Detection -- Biometrics and Identification -- Exposing Presentation Attacks by a Combination of Multi-intrinsic Image Properties, Convolutional Networks and Transfer Learning -- Multiview 3D Markerless Human Pose Estimation -- Clip-level Feature Aggregation: A Key Factor for Video-based Person Re-Identification -- Towards Approximating Personality Cues Through Simple Daily Activities -- Person Identification by Walking Gesture using Skeleton Sequences -- Verifying Kinship from RGB-D Face Data -- VA-StarGAN: Continuous Affect Generation -- Fast Iris Segmentation Algorithm for Visible Wavelength Images Based on Multi-Color Space -- A Local Flow Phase Stretch Transform for Robust Retinal Vessel Detection -- Evaluation of Unconditioned Deep Generative Synthesis of Retinal Images -- Image Analysis -- Dynamic Texture Representation Based on Hierarchical Local Patterns -- Temporal-clustering based Technique for Identifying Thermal Regions in Buildings -- Distance Weighted Loss for Forest Trail Detection using Semantic Line -- Localization of Map Changes by Exploiting SLAM Residuals -- Initial Pose Estimation of 3D Object with Severe Occlusion Using Deep Learning -- Automatic Focal Blur Segmentation based on Difference of Blur Feature using Theoretical Thresholding and Graphcuts -- Feature Map Augmentation to Improve Rotation Invariance in Convolutional Neural Networks -- Automatic Optical Inspection for Millimeter Scale Probe Surface Stripping Defects using Convolutional Neural Network -- Image restauration, Compression and Watermarking -- A New SVM-based Zero-watermarking Technique for3D Videos Traitor Tracing -- Design of Perspective Affine Motion Compensation for Versatile Video Coding (VVC) -- Investigation of Coding Standards Performances on Optically Acquired and Synthetic Holograms -- Natural Images Enhancement Using Structure Extraction and Retinex -- Unsupervised Desmoking of Laparoscopy Images using Multi-scale DesmokeNet -- VLW-Net: A Very Light-Weight Convolutional Neural Network (CNN) for Single Image Dehazing -- An Improved GAN Semantic Image Inpainting -- Tracking, Mapping and Scene Analysis -- CUDA Implementation of a Point Cloud Shape Descriptor Method for Archaeological Studies -- Red-Green-Blue Augmented Reality Tags for Retail Stores -- Guided Stereo to Improve Depth Resolution of a Small Baseline Stereo Camera Using an Image Sequence -- SuperNCN: Neighbourhood Consensus Network for Robust Outdoor Scenes Matching -- Using Normal/ Abnormal Video Sequence Categorization to Efficient Facial Expression Recognition in the Wild -- Distributed Multi-Class Road User Tracking in Multi-Camera Network for Smart Traffic Applications -- Vehicles Tracking by combining Convolutional Neural Network based Segmentation and Optical Flow Estimation -- Real Time Embedded Person Detection and Tracking in Camera Streams -- Learning Target-Specific Response Attention for Siamese Network Based Visual Tracking. |
| Record Nr. | UNISA-996418209603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Advanced Concepts for Intelligent Vision Systems : 20th International Conference, ACIVS 2020, Auckland, New Zealand, February 10–14, 2020, Proceedings / / edited by Jacques Blanc-Talon, Patrice Delmas, Wilfried Philips, Dan Popescu, Paul Scheunders
| Advanced Concepts for Intelligent Vision Systems : 20th International Conference, ACIVS 2020, Auckland, New Zealand, February 10–14, 2020, Proceedings / / edited by Jacques Blanc-Talon, Patrice Delmas, Wilfried Philips, Dan Popescu, Paul Scheunders |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XV, 568 p. 305 illus., 231 illus. in color.) |
| Disciplina |
006.37
006.6 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Computer engineering Computer networks Application software Machine learning Education - Data processing Social sciences - Data processing Computer Vision Computer Engineering and Networks Computer and Information Systems Applications Machine Learning Computers and Education Computer Application in Social and Behavioral Sciences |
| ISBN | 3-030-40605-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep Learning -- Desing data model for Big Data Analysis System -- Deep Learning-based Techniques for Plant Diseases Recognition in Real-Field Scenarios -- EpNet: a Deep Neural Network for Ear Detection in 3D Point Clouds -- Fire Segmentation in Still Images -- Region Proposal Oriented Approach for Domain Adaptive Object Detection -- Deep Convolutional Network-Based Framework for Melanoma Lesion Detection and Segmentation -- A Novel Framework for Early Fire Detection Using Terrestrial and Aerial 360-degree Images -- Biomedical Image Analysis -- Segmentation of Phase-Contrast MR Images for Aortic Pulse Wave Velocity Measurements -- On the Uncertainty of Retinal Artery-vein Classification with Dense Fully-convolutional Neural Networks -- Object Contour Refinement using Instance Segmentation in Dental Images -- Correction of Temperature Estimated from a Low-Cost Handheld Infrared Camera for Clinical Monitoring -- Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic -- Deep-Learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography -- Quadratic Tensor Anisotropy Measures for Reliable Curvilinear Pattern Detection -- Biometrics and Identification -- Exposing Presentation Attacks by a Combination of Multi-intrinsic Image Properties, Convolutional Networks and Transfer Learning -- Multiview 3D Markerless Human Pose Estimation -- Clip-level Feature Aggregation: A Key Factor for Video-based Person Re-Identification -- Towards Approximating Personality Cues Through Simple Daily Activities -- Person Identification by Walking Gesture using Skeleton Sequences -- Verifying Kinship from RGB-D Face Data -- VA-StarGAN: Continuous Affect Generation -- Fast Iris Segmentation Algorithm for Visible Wavelength Images Based on Multi-Color Space -- A Local Flow Phase Stretch Transform for Robust Retinal Vessel Detection -- Evaluation of Unconditioned Deep Generative Synthesis of Retinal Images -- Image Analysis -- Dynamic Texture Representation Based on Hierarchical Local Patterns -- Temporal-clustering based Technique for Identifying Thermal Regions in Buildings -- Distance Weighted Loss for Forest Trail Detection using Semantic Line -- Localization of Map Changes by Exploiting SLAM Residuals -- Initial Pose Estimation of 3D Object with Severe Occlusion Using Deep Learning -- Automatic Focal Blur Segmentation based on Difference of Blur Feature using Theoretical Thresholding and Graphcuts -- Feature Map Augmentation to Improve Rotation Invariance in Convolutional Neural Networks -- Automatic Optical Inspection for Millimeter Scale Probe Surface Stripping Defects using Convolutional Neural Network -- Image restauration, Compression and Watermarking -- A New SVM-based Zero-watermarking Technique for3D Videos Traitor Tracing -- Design of Perspective Affine Motion Compensation for Versatile Video Coding (VVC) -- Investigation of Coding Standards Performances on Optically Acquired and Synthetic Holograms -- Natural Images Enhancement Using Structure Extraction and Retinex -- Unsupervised Desmoking of Laparoscopy Images using Multi-scale DesmokeNet -- VLW-Net: A Very Light-Weight Convolutional Neural Network (CNN) for Single Image Dehazing -- An Improved GAN Semantic Image Inpainting -- Tracking, Mapping and Scene Analysis -- CUDA Implementation of a Point Cloud Shape Descriptor Method for Archaeological Studies -- Red-Green-Blue Augmented Reality Tags for Retail Stores -- Guided Stereo to Improve Depth Resolution of a Small Baseline Stereo Camera Using an Image Sequence -- SuperNCN: Neighbourhood Consensus Network for Robust Outdoor Scenes Matching -- Using Normal/ Abnormal Video Sequence Categorization to Efficient Facial Expression Recognition in the Wild -- Distributed Multi-Class Road User Tracking in Multi-Camera Network for Smart Traffic Applications -- Vehicles Tracking by combining Convolutional Neural Network based Segmentation and Optical Flow Estimation -- Real Time Embedded Person Detection and Tracking in Camera Streams -- Learning Target-Specific Response Attention for Siamese Network Based Visual Tracking. |
| Record Nr. | UNINA-9910380758903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced Data Analytics Using Python : With Architectural Patterns, Text and Image Classification, and Optimization Techniques / / by Sayan Mukhopadhyay, Pratip Samanta
| Advanced Data Analytics Using Python : With Architectural Patterns, Text and Image Classification, and Optimization Techniques / / by Sayan Mukhopadhyay, Pratip Samanta |
| Autore | Mukhopadhyay Sayan |
| Edizione | [2nd ed. 2023.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
| Descrizione fisica | 1 online resource (259 pages) |
| Disciplina | 006.312 |
| Soggetto topico |
Python (Computer program language)
Machine learning Data mining |
| ISBN | 1-4842-8005-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Overview of Python Language -- Chapter 2: ETL with Python -- Chapter 3: Supervised Learning and Unsupervised Learning with Python -- Chapter 4: Clustering with Python -- Chapter 5: Deep Learning & Neural Networks -- Chapter 6: Time Series Analysis -- Chapter 7: Analytics in Scale. |
| Record Nr. | UNINA-9910632475203321 |
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Mukhopadhyay Sayan
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced deep learning for engineers and scientists : a practical approach / / edited by G. R. Kanagachidambaresan [and three others]
| Advanced deep learning for engineers and scientists : a practical approach / / edited by G. R. Kanagachidambaresan [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (293 pages) |
| Disciplina | 006.31 |
| Collana | EAI/Springer Innovations in Communication and Computing |
| Soggetto topico |
Computational intelligence
Electrical engineering Machine learning |
| ISBN | 3-030-66519-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910495246403321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more / / Rowel Atienza
| Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more / / Rowel Atienza |
| Autore | Atienza Rowel |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, England : , : Packt Publishing, Limited, , [2018] |
| Descrizione fisica | 1 online resource (368 pages) |
| Disciplina | 006.32 |
| Soggetto topico |
Machine learning
Neural networks (Computer science) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Copyright -- Packt upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introducing Advanced Deep Learning with Keras -- Why is Keras the perfect deep learning library? -- Installing Keras and TensorFlow -- Implementing the core deep learning models - MLPs, CNNs and RNNs -- The difference between MLPs, CNNs, and RNNs -- Multilayer perceptrons (MLPs) -- MNIST dataset -- MNIST digits classifier model -- Building a model using MLPs and Keras -- Regularization -- Output activation and loss function -- Optimization -- Performance evaluation -- Model summary -- Convolutional neural networks (CNNs) -- Convolution -- Pooling operations -- Performance evaluation and model summary -- Recurrent neural networks (RNNs) -- Conclusion -- Chapter 2: Deep Neural Networks -- Functional API -- Creating a two-input and one-output model -- Deep residual networks (ResNet) -- ResNet v2 -- Densely connected convolutional networks (DenseNet) -- Building a 100-layer DenseNet-BC for CIFAR10 -- Conclusion -- References -- Chapter 3: Autoencoders -- Principles of autoencoders -- Building autoencoders using Keras -- Denoising autoencoder (DAE) -- Automatic colorization autoencoder -- Conclusion -- References -- Chapter 4: Generative Adversarial Networks (GANs) -- An overview of GANs -- Principles of GANs -- GAN implementation in Keras -- Conditional GAN -- Conclusion -- References -- Chapter 5: Improved GANs -- Wasserstein GAN -- Distance functions -- Distance function in GANs -- Use of Wasserstein loss -- WGAN implementation using Keras -- Least-squares GAN (LSGAN) -- Auxiliary classifier GAN (ACGAN) -- Conclusion -- References -- Chapter 6: Disentangled Representation GANs -- Disentangled representations -- InfoGAN -- Implementation of InfoGAN in Keras -- Generator outputs of InfoGAN -- StackedGAN -- Implementation of StackedGAN in Keras.
Generator outputs of StackedGAN -- Conclusion -- Reference -- Chapter 7: Cross-Domain GANs -- Principles of CycleGAN -- The CycleGAN Model -- Implementing CycleGAN using Keras -- Generator outputs of CycleGAN -- CycleGAN on MNIST and SVHN datasets -- Conclusion -- References -- Chapter 8: Variational Autoencoders (VAEs) -- Principles of VAEs -- Variational inference -- Core equation -- Optimization -- Reparameterization trick -- Decoder testing -- VAEs in Keras -- Using CNNs for VAEs -- Conditional VAE (CVAE) -- -VAE: VAE with disentangled latent representations -- Conclusion -- References -- Chapter 9: Deep Reinforcement Learning -- Principles of reinforcement learning (RL) -- The Q value -- Q-Learning example -- Q-Learning in Python -- Nondeterministic environment -- Temporal-difference learning -- Q-Learning on OpenAI gym -- Deep Q-Network (DQN) -- DQN on Keras -- Double Q-Learning (DDQN) -- Conclusion -- References -- Chapter 10: Policy Gradient Methods -- Policy gradient theorem -- Monte Carlo policy gradient (REINFORCE) method -- REINFORCE with baseline method -- Actor-Critic method -- Advantage Actor-Critic (A2C) method -- Policy Gradient methods with Keras -- Performance evaluation of policy gradient methods -- Conclusion -- References -- Other Books You May Enjoy -- Index. |
| Record Nr. | UNINA-9910795323903321 |
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Atienza Rowel
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| London, England : , : Packt Publishing, Limited, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more / / Rowel Atienza
| Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more / / Rowel Atienza |
| Autore | Atienza Rowel |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, England : , : Packt Publishing, Limited, , [2018] |
| Descrizione fisica | 1 online resource (368 pages) |
| Disciplina | 006.32 |
| Soggetto topico |
Machine learning
Neural networks (Computer science) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Copyright -- Packt upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introducing Advanced Deep Learning with Keras -- Why is Keras the perfect deep learning library? -- Installing Keras and TensorFlow -- Implementing the core deep learning models - MLPs, CNNs and RNNs -- The difference between MLPs, CNNs, and RNNs -- Multilayer perceptrons (MLPs) -- MNIST dataset -- MNIST digits classifier model -- Building a model using MLPs and Keras -- Regularization -- Output activation and loss function -- Optimization -- Performance evaluation -- Model summary -- Convolutional neural networks (CNNs) -- Convolution -- Pooling operations -- Performance evaluation and model summary -- Recurrent neural networks (RNNs) -- Conclusion -- Chapter 2: Deep Neural Networks -- Functional API -- Creating a two-input and one-output model -- Deep residual networks (ResNet) -- ResNet v2 -- Densely connected convolutional networks (DenseNet) -- Building a 100-layer DenseNet-BC for CIFAR10 -- Conclusion -- References -- Chapter 3: Autoencoders -- Principles of autoencoders -- Building autoencoders using Keras -- Denoising autoencoder (DAE) -- Automatic colorization autoencoder -- Conclusion -- References -- Chapter 4: Generative Adversarial Networks (GANs) -- An overview of GANs -- Principles of GANs -- GAN implementation in Keras -- Conditional GAN -- Conclusion -- References -- Chapter 5: Improved GANs -- Wasserstein GAN -- Distance functions -- Distance function in GANs -- Use of Wasserstein loss -- WGAN implementation using Keras -- Least-squares GAN (LSGAN) -- Auxiliary classifier GAN (ACGAN) -- Conclusion -- References -- Chapter 6: Disentangled Representation GANs -- Disentangled representations -- InfoGAN -- Implementation of InfoGAN in Keras -- Generator outputs of InfoGAN -- StackedGAN -- Implementation of StackedGAN in Keras.
Generator outputs of StackedGAN -- Conclusion -- Reference -- Chapter 7: Cross-Domain GANs -- Principles of CycleGAN -- The CycleGAN Model -- Implementing CycleGAN using Keras -- Generator outputs of CycleGAN -- CycleGAN on MNIST and SVHN datasets -- Conclusion -- References -- Chapter 8: Variational Autoencoders (VAEs) -- Principles of VAEs -- Variational inference -- Core equation -- Optimization -- Reparameterization trick -- Decoder testing -- VAEs in Keras -- Using CNNs for VAEs -- Conditional VAE (CVAE) -- -VAE: VAE with disentangled latent representations -- Conclusion -- References -- Chapter 9: Deep Reinforcement Learning -- Principles of reinforcement learning (RL) -- The Q value -- Q-Learning example -- Q-Learning in Python -- Nondeterministic environment -- Temporal-difference learning -- Q-Learning on OpenAI gym -- Deep Q-Network (DQN) -- DQN on Keras -- Double Q-Learning (DDQN) -- Conclusion -- References -- Chapter 10: Policy Gradient Methods -- Policy gradient theorem -- Monte Carlo policy gradient (REINFORCE) method -- REINFORCE with baseline method -- Actor-Critic method -- Advantage Actor-Critic (A2C) method -- Policy Gradient methods with Keras -- Performance evaluation of policy gradient methods -- Conclusion -- References -- Other Books You May Enjoy -- Index. |
| Record Nr. | UNINA-9910819310903321 |
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Atienza Rowel
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| London, England : , : Packt Publishing, Limited, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced deep learning with TensorFlow 2 and Keras : apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more / / Rowel Atienza
| Advanced deep learning with TensorFlow 2 and Keras : apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more / / Rowel Atienza |
| Autore | Atienza Rowel |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Birmingham, UK : , : Packt Publishing, , 2020 |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Soggetto topico |
Artificial intelligence
Machine learning Python (Computer program language) Neural networks (Computer science) |
| ISBN | 1-83882-572-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910795281003321 |
|
Atienza Rowel
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| Birmingham, UK : , : Packt Publishing, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced deep learning with TensorFlow 2 and Keras : apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more / / Rowel Atienza
| Advanced deep learning with TensorFlow 2 and Keras : apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more / / Rowel Atienza |
| Autore | Atienza Rowel |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Birmingham, UK : , : Packt Publishing, , 2020 |
| Descrizione fisica | 1 online resource (1 volume) : illustrations |
| Soggetto topico |
Artificial intelligence
Machine learning Python (Computer program language) Neural networks (Computer science) |
| ISBN | 1-83882-572-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910826422303321 |
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Atienza Rowel
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| Birmingham, UK : , : Packt Publishing, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced Engineering, Technology and Applications [[electronic resource] ] : Second International Conference, ICAETA 2023, Istanbul, Turkey, March 10–11, 2023, Revised Selected Papers / / edited by Alessandro Ortis, Alaa Ali Hameed, Akhtar Jamil
| Advanced Engineering, Technology and Applications [[electronic resource] ] : Second International Conference, ICAETA 2023, Istanbul, Turkey, March 10–11, 2023, Revised Selected Papers / / edited by Alessandro Ortis, Alaa Ali Hameed, Akhtar Jamil |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (XIV, 504 p. 209 illus., 175 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Machine learning Application software Image processing - Digital techniques Computer vision Computer engineering Computer networks Artificial Intelligence Machine Learning Computer and Information Systems Applications Computer Imaging, Vision, Pattern Recognition and Graphics Computer Vision Computer Engineering and Networks |
| ISBN | 3-031-50920-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Pattern Recognition and Machine Learning -- Computer Vision and Applications -- Modeling and Computational Intelligence. |
| Record Nr. | UNINA-9910799246903321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning
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