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Deep Learning for Genomics: Data-driven approaches for genomics applications in life sciences and biotechnology
by Upendra Kumar DevisettyLearn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industriesKey FeaturesApply deep learning algorithms to solve real-world problems in the field of genomicsExtract biological insights from deep learning models built from genomic datasetsTrain, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomicsBook DescriptionDeep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics.What you will learnDiscover the machine learning applications for genomicsExplore deep learning concepts and methodologies for genomics applicationsUnderstand supervised deep learning algorithms for genomics applicationsGet to grips with unsupervised deep learning with autoencodersImprove deep learning models using generative modelsOperationalize deep learning models from genomics datasetsVisualize and interpret deep learning modelsUnderstand deep learning challenges, pitfalls, and best practicesWho this book is forThis deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomics. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.
Deep Learning for Human Activity Recognition: Second International Workshop, DL-HAR 2020, Held in Conjunction with IJCAI-PRICAI 2020, Kyoto, Japan, January 8, 2021, Proceedings (Communications in Computer and Information Science #1370)
by Xiaoli Li Min Wu Zhenghua Chen Le ZhangThis book constitutes refereed proceedings of the Second International Workshop on Deep Learning for Human Activity Recognition, DL-HAR 2020, held in conjunction with IJCAI-PRICAI 2020, in Kyoto, Japan, in January 2021. Due to the COVID-19 pandemic the workshop was postponed to the year 2021 and held in a virtual format. The 10 presented papers were thorougly reviewed and included in the volume. They present recent research on applications of human activity recognition for various areas such as healthcare services, smart home applications, and more.
Deep Learning for Hydrometeorology and Environmental Science (Water Science and Technology Library #99)
by Vijay P. Singh Taesam Lee Kyung Hwa ChoThis book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited.Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
Deep Learning for Hyperspectral Image Analysis and Classification (Engineering Applications of Computational Methods #5)
by Linmi Tao Atif MugheesThis book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
Deep Learning for Internet of Things Infrastructure
by Al-Sakib Khan Pathan Mamoun Alazab Uttam Ghosh Ali Kashif BashirThis book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of deep learning (DL)–based data analytics of IoT (Internet of Things) infrastructures. Deep Learning for Internet of Things Infrastructure addresses emerging trends and issues on IoT systems and services across various application domains. The book investigates the challenges posed by the implementation of deep learning on IoT networking models and services. It provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT. The book also explores new functions and technologies to provide adaptive services and intelligent applications for different end users. FEATURES Promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of DL-based data analytics of IoT infrastructures Addresses emerging trends and issues on IoT systems and services across various application domains Investigates the challenges posed by the implementation of deep learning on IoT networking models and services Provides fundamental theory, model, and methodology in interpreting, aggregating, processing, and analyzing data for intelligent DL-enabled IoT Explores new functions and technologies to provide adaptive services and intelligent applications for different end users Uttam Ghosh is an Assistant Professor in the Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA. Mamoun Alazab is an Associate Professor in the College of Engineering, IT and Environment at Charles Darwin University, Australia. Ali Kashif Bashir is a Senior Lecturer/Associate Professor and Program Leader of BSc (H) Computer Forensics and Security at the Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom. Al-Sakib Khan Pathan is an Adjunct Professor of Computer Science and Engineering at the Independent University, Bangladesh.
Deep Learning for Medical Decision Support Systems (Studies in Computational Intelligence #909)
by Bogdan Patrut Utku Kose Omer Deperlioglu Jafar AlzubiThis book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.
Deep Learning for Multimedia Processing Applications: Volume One: Image Security and Intelligent Systems for Multimedia Processing
by Mengxing Huang Muhammad Aamir Uzair Aslam Bhatti Jingbing Li Sibghat Ullah BazaiDeep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing. Divided into two volumes, Volume One begins by introducing the fundamental concepts of deep learning, providing readers with a solid foundation to understand its relevance in multimedia processing. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos. Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts. Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.
Deep Learning for Multimedia Processing Applications: Volume Two: Signal Processing and Pattern Recognition
by Mengxing Huang Muhammad Aamir Uzair Aslam Bhatti Jingbing Li Sibghat Ullah BazaiDeep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing. Divided into two volumes, Volume Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos. Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts. Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.
Deep Learning for NLP and Speech Recognition
by Uday Kamath John Liu James WhitakerThis textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
Deep Learning for Natural Language Processing
by Stephan RaaijmakersExplore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning!Inside Deep Learning for Natural Language Processing you&’ll find a wealth of NLP insights, including: An overview of NLP and deep learning One-hot text representations Word embeddings Models for textual similarity Sequential NLP Semantic role labeling Deep memory-based NLP Linguistic structure Hyperparameters for deep NLP Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms. About the technology Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses. About the book Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You&’ll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses! What's inside Improve question answering with sequential NLP Boost performance with linguistic multitask learning Accurately interpret linguistic structure Master multiple word embedding techniques About the reader For readers with intermediate Python skills and a general knowledge of NLP. No experience with deep learning is required. About the author Stephan Raaijmakers is professor of Communicative AI at Leiden University and a senior scientist at The Netherlands Organization for Applied Scientific Research (TNO). Table of Contents PART 1 INTRODUCTION 1 Deep learning for NLP 2 Deep learning and language: The basics 3 Text embeddings PART 2 DEEP NLP 4 Textual similarity 5 Sequential NLP 6 Episodic memory for NLP PART 3 ADVANCED TOPICS 7 Attention 8 Multitask learning 9 Transformers 10 Applications of Transformers: Hands-on with BERT
Deep Learning for Natural Language Processing: Creating Neural Networks with Python
by Palash Goyal Sumit Pandey Karan JainDiscover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models.You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system.This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways.What You Will LearnGain the fundamentals of deep learning and its mathematical prerequisitesDiscover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classificationWho This Book Is ForSoftware developers who are curious to try out deep learning with NLP.
Deep Learning for Natural Language Processing: Solve your natural language processing problems with smart deep neural networks
by Karthiek Reddy Bokka Shubhangi Hora Tanuj Jain Monicah WambuguGain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues.Key FeaturesGain insights into the basic building blocks of natural language processingLearn how to select the best deep neural network to solve your NLP problemsExplore convolutional and recurrent neural networks and long short-term memory networksBook DescriptionApplying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues.What you will learnUnderstand various pre-processing techniques for deep learning problemsBuild a vector representation of text using word2vec and GloVeCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLPBuild a machine translation model in KerasDevelop a text generation application using LSTMBuild a trigger word detection application using an attention modelWho this book is forIf you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.
Deep Learning for Polymer Discovery: Foundation and Advances (Synthesis Lectures on Data Mining and Knowledge Discovery)
by Gang Liu Meng Jiang Eric InaeThis book presents a comprehensive range of topics in deep learning for polymer discovery, from fundamental concepts to advanced methodologies. These topics are crucial as they address critical challenges in polymer science and engineering. With a growing demand for new materials with specific properties, traditional experimental methods for polymer discovery are becoming increasingly time-consuming and costly. Deep learning offers a promising solution by enabling rapid screening of potential polymers and accelerating the design process. The authors begin with essential knowledge on polymer data representations and neural network architectures, then progress to deep learning frameworks for property prediction and inverse polymer design. The book then explores both sequence-based and graph-based approaches, covering various neural network types including LSTMs, GRUs, GCNs, and GINs. Advanced topics include interpretable graph deep learning with environment-based augmentation, semi-supervised techniques for addressing label imbalance, and data-centric transfer learning using diffusion models. The book aims to solve key problems in polymer discovery, including accurate property prediction, efficient design of polymers with desired characteristics, model interpretability, handling imbalanced and limited labeled data, and leveraging unlabeled data to improve prediction accuracy.
Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems (Power Electronics and Power Systems)
by Yan Du Fangxing LiThis book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control.Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers.Provides a history of AI in power grid operation and planning;Introduces deep learning algorithms and applications in power systems;Includes several representative case studies.
Deep Learning for Remote Sensing Images with Open Source Software (Signal and Image Processing of Earth Observations)
by Rémi CressonIn today’s world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data. Specific Features of this Book: The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow) Presents approaches suited for real world images and data targeting large scale processing and GIS applications Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills. Includes deep learning techniques through many step by step remote sensing data processing exercises.
Deep Learning for Search
by Tommaso TeofiliSummaryDeep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques. By the time you're finished with the book, you'll be ready to build amazing search engines that deliver the results your users need and that get better as time goes on!Foreword by Chris Mattmann.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyDeep learning handles the toughest search challenges, including imprecise search terms, badly indexed data, and retrieving images with minimal metadata. And with modern tools like DL4J and TensorFlow, you can apply powerful DL techniques without a deep background in data science or natural language processing (NLP). This book will show you how.About the BookDeep Learning for Search teaches you to improve your search results with neural networks. You'll review how DL relates to search basics like indexing and ranking. Then, you'll walk through in-depth examples to upgrade your search with DL techniques using Apache Lucene and Deeplearning4j. As the book progresses, you'll explore advanced topics like searching through images, translating user queries, and designing search engines that improve as they learn!What's insideAccurate and relevant rankingsSearching across languagesContent-based image searchSearch with recommendationsAbout the ReaderFor developers comfortable with Java or a similar language and search basics. No experience with deep learning or NLP needed.About the AuthorTommaso Teofili is a software engineer with a passion for open source and machine learning. As a member of the Apache Software Foundation, he contributes to a number of open source projects, ranging from topics like information retrieval (such as Lucene and Solr) to natural language processing and machine translation (including OpenNLP, Joshua, and UIMA).He currently works at Adobe, developing search and indexing infrastructure components, and researching the areas of natural language processing, information retrieval, and deep learning. He has presented search and machine learning talks at conferences including BerlinBuzzwords, International Conference on Computational Science, ApacheCon, EclipseCon, and others. You can find him on Twitter at @tteofili. Table of ContentsPART 1 - SEARCH MEETS DEEP LEARNINGNeural searchGenerating synonymsPART 2 - THROWING NEURAL NETS AT A SEARCH ENGINEFrom plain retrieval to text generationMore-sensitive query suggestionsRanking search results with word embeddingsDocument embeddings for rankings and recommendationsPART 3 - ONE STEP BEYONDSearching across languagesContent-based image searchA peek at performance
Deep Learning for Security and Privacy Preservation in IoT (Signals and Communication Technology)
by Neeraj Kumar Aaisha MakkarThis book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyber-attacks and proposes deep learning-based approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of existing solutions to cover the entire IoT network security spectrum, the book utilizes artificial neural network models, which are used to classify, recognize, and model complex data including images, voice, and text, to enhance the level of security and privacy of IoT. This is applied to several IoT applications which include wireless sensor networks (WSN), meter reading transmission in smart grid, vehicular ad hoc networks (VANET), industrial IoT and connected networks. The book serves as a reference for researchers, academics, and network engineers who want to develop enhanced security and privacy features in the design of IoT systems.
Deep Learning for Seismic Data Enhancement and Representation (Advances in Oil and Gas Exploration & Production)
by Jiefu Chen Xuqing Wu Shirui Wang Wenyi HuSeismic imaging is a key component of subsurface exploration, and it depends on a high-quality seismic data acquisition system with effective seismic processing algorithms. Seismic data quality concerns various factors such as acquisition design, environmental constraints, sampling resolution, and noises. The focus of this book is to investigate efficient seismic data representation and signal enhancement solutions by leveraging the powerful feature engineering capability of deep learning. The book delves into seismic data representation and enhancement issues, ranging from seismic acquisition design to subsequent quality improvement and compression technologies. Given the challenges of obtaining suitable labeled training datasets for seismic data processing problems, we concentrate on exploring deep learning approaches that eliminate the need for labels. We combined novel deep learning techniques with conventional seismic data processing methods, and construct networks and frameworks tailored for seismic data processing. The editors and authors of this book come from both academia and industry with hands-on experiences in seismic data processing and imaging.
Deep Learning for Smart Healthcare: Trends, Challenges and Applications
by S. Poonkuntran B. Sundaravadivazhagan K. Murugeswari Thendral PuyalnithiDeep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.
Deep Learning for Social Media Data Analytics (Studies in Big Data #113)
by Tzung-Pei Hong Anupam Biswas Leticia Serrano-Estrada Akrati SaxenaThis edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics.
Deep Learning for Targeted Treatments: Transformation in Healthcare
by Balamurugan Balusamy Gheorghita Ghinea Rajesh Kumar Dhanaraj Rishabha Malviya Sonali SundramDEEP LEARNING FOR TREATMENTS The book provides the direction for future research in deep learning in terms of its role in targeted treatment, biological systems, site-specific drug delivery, risk assessment in therapy, etc. Deep Learning for Targeted Treatments describes the importance of the deep learning framework for patient care, disease imaging/detection, and health management. Since deep learning can and does play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare. Audience The book will be immensely interesting and useful to researchers and those working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.
Deep Learning for Unmanned Systems (Studies in Computational Intelligence #984)
by Anis Koubaa Ahmad Taher AzarThis book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS)The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references.The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques.The book chapters are lucidly illustrated with numerical examples and simulations.The book chapters discuss details of applications and future research areas.
Deep Learning for Video Understanding (Wireless Networks)
by Zuxuan Wu Yu-Gang JiangThis book presents deep learning techniques for video understanding. For deep learning basics, the authors cover machine learning pipelines and notations, 2D and 3D Convolutional Neural Networks for spatial and temporal feature learning. For action recognition, the authors introduce classical frameworks for image classification, and then elaborate both image-based and clip-based 2D/3D CNN networks for action recognition. For action detection, the authors elaborate sliding windows, proposal-based detection methods, single stage and two stage approaches, spatial and temporal action localization, followed by datasets introduction. For video captioning, the authors present language-based models and how to perform sequence to sequence learning for video captioning. For unsupervised feature learning, the authors discuss the necessity of shifting from supervised learning to unsupervised learning and then introduce how to design better surrogate training tasks to learn video representations. Finally, the book introduces recent self-training pipelines like contrastive learning and masked image/video modeling with transformers. The book provides promising directions, with an aim to promote future research outcomes in the field of video understanding with deep learning.
Deep Learning for Vision Systems
by Mohamed ElgendyHow does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition.Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you&’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology How much has computer vision advanced? One ride in a Tesla is the only answer you&’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway. About the book How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. What's inside Image classification and object detection Advanced deep learning architectures Transfer learning and generative adversarial networks DeepDream and neural style transfer Visual embeddings and image search About the reader For intermediate Python programmers. About the author Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio. Table of Contents PART 1 - DEEP LEARNING FOUNDATION 1 Welcome to computer vision 2 Deep learning and neural networks 3 Convolutional neural networks 4 Structuring DL projects and hyperparameter tuning PART 2 - IMAGE CLASSIFICATION AND DETECTION 5 Advanced CNN architectures 6 Transfer learning 7 Object detection with R-CNN, SSD, and YOLO PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS 8 Generative adversarial networks (GANs) 9 DeepDream and neural style transfer 10 Visual embeddings
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
by Peter Eastman Bharath Ramsundar Vijay Pande Pat WaltersDeep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields.Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges.Learn the basics of performing machine learning on molecular dataUnderstand why deep learning is a powerful tool for genetics and genomicsApply deep learning to understand biophysical systemsGet a brief introduction to machine learning with DeepChemUse deep learning to analyze microscopic imagesAnalyze medical scans using deep learning techniquesLearn about variational autoencoders and generative adversarial networksInterpret what your model is doing and how it’s working