Deep Learning Applications, Volume 3 PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Deep Learning Applications, Volume 3 PDF full book. Access full book title Deep Learning Applications, Volume 3 by M. Arif Wani. Download full books in PDF and EPUB format.
Author: M. Arif Wani
Publisher:
ISBN: 9789811633584
Category :
Languages : en
Pages : 0
Get Book
Book Description
This 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. .
Author: M. Arif Wani
Publisher:
ISBN: 9789811633584
Category :
Languages : en
Pages : 0
Get Book
Book Description
This 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. .
Author: M. Arif Wani
Publisher: Springer Nature
ISBN: 9811633576
Category : Technology & Engineering
Languages : en
Pages : 328
Get Book
Book Description
This 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.
Author: Valentina Emilia Balas
Publisher: Springer
ISBN: 3030114791
Category : Technology & Engineering
Languages : en
Pages : 383
Get Book
Book Description
This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
Author: M. Arif Wani
Publisher:
ISBN: 9789811567605
Category :
Languages : en
Pages : 0
Get Book
Book Description
This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
Author: M. Arif Wani
Publisher: Springer Nature
ISBN: 9811961530
Category : Technology & Engineering
Languages : en
Pages : 394
Get Book
Book Description
This book presents a compilation of extended versions of selected papers from 20th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2021). It focuses on deep learning networks and their applications in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers. The book is fourth in the series published since 2017.
Author: Pier Luigi Mazzeo
Publisher: BoD – Books on Demand
ISBN: 1839623748
Category : Computers
Languages : en
Pages : 216
Get Book
Book Description
Deep learning is a branch of machine learning similar to artificial intelligence. The applications of deep learning vary from medical imaging to industrial quality checking, sports, and precision agriculture. This book is divided into two sections. The first section covers deep learning architectures and the second section describes the state of the art of applications based on deep learning.
Author: Information Reso Management Association
Publisher:
ISBN: 9781668432051
Category :
Languages : en
Pages : 604
Get Book
Book Description
Author: Zied Driss
Publisher: Bentham Science Publishers
ISBN: 9815179284
Category : Science
Languages : en
Pages : 134
Get Book
Book Description
This book focuses on cases and studies of interest to mechanical engineers and industrial technicians. The considered applications in this volume are widely used in several industrial fields particularly in the automotive and aviation industries. Readers will understand the theory and techniques which are used in each application covered in each chapter. Volume 3 includes the following topics: Numerical simulations of three-dimensional laminar mixed convection heat transfer of water-based-Al2O3 nanofluid in an open cubic cavity with a heated block. Nonlinear formulations of Element-Free Galerkin Method (EFGM) for large deformation analysis of Ogden’s hyperelastic materials, emphasizing incompressibility and mesh distortion avoidance. Development of a 3D numerical model with LS-DYNA using a coupled SPH-FEM method to simulate hydraulic behavior of a Ski-Jump Spillway with dentates, showcasing precision through validation. Exploration of enhancing the inlet system of an LPG-H2 fueled engine using a static inclined blade turbine, analyzed through Computational Fluid Dynamics (CFD) simulations. Effective utilization of Artificial Neural Networks (ANN) in heat transfer applications, addressing issues like fouling in heat exchangers, showcasing their accuracy compared to experimental data. Investigation of the impact of nitrogen concentration on the structure and properties of ZrN coatings deposited by magnetron sputtering, evaluating variations in structural and mechanical properties. Forced convection in a horizontal cylindrical pipe with pseudoplastic fluid, considering uniform constant heat flux and uniform temperature as boundary conditions. Modeling and experimental study of a water solar collector coupled to an optimized solar still, aiming to enhance freshwater production in a solar distillation system under specific climatic conditions. Exploration of the effect of film thickness on the structure and properties of Ti-N films deposited by magnetron sputtering, utilizing theoretical and experimental analysis to confirm the rock salt TiN structure. The presented case studies and development approaches aim to provide readers with basic and applied information broadly related to mechanical engineering and technology. Readership Graduate students, PhD candidates and professionals seeking basic and applied information related to mechanical engineering and technology.
Author: M. Arif Wani
Publisher: Springer Nature
ISBN: 981156759X
Category : Technology & Engineering
Languages : en
Pages : 307
Get Book
Book Description
This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319944630
Category : Computers
Languages : en
Pages : 497
Get Book
Book Description
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
