Download or read book Deep Learning for Unstructured Data by Leveraging Domain Knowledge written by Shanshan Zhang and published by . This book was released on 2019 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unstructured data such as texts, strings, images, audios, videos are everywhere due to the social interaction on the Internet and the high-throughput technology in sciences, e.g., chemistry and biology. However, for traditional machine learning algorithms, classifying a text document is far more difficult than classifying a data entry in a spreadsheet. We have to convert the unstructured data into some numeric vectors which can then be understood by machine learning algorithms. For example, a sentence is first converted to a vector of word counts, and then fed into a classification algorithm such as logistic regression and support vector machine. The creation of such numerical vectors is very challenging and difficult. Recent progress in deep learning provides us a new way to jointly learn features and train classifiers for unstructured data. For example, recurrent neural networks proved successful at learning from a sequence of word indices; convolutional neural networks are effective to learn from videos, which are sequences of pixel matrices. Our research focuses on developing novel deep learning approaches for text and graph data. Breakthroughs using deep learning have been made during the last few years for many core tasks in natural language processing, such as machine translation, POS tagging, named entity recognition, etc. However, when it comes to informal and noisy text data, such as tweets, HTMLs, OCR, there are two major issues with modern deep learning technologies. First, deep learning requires large amount of labeled data to train an effective model; second, neural network architectures that work with natural language are not proper with informal text. In this thesis, we address the two important issues and develop new deep learning approaches in four supervised and unsupervised tasks with noisy text. We first present a deep feature engineering approach for informative tweets discovery during the emerging disasters. We propose to use unlabeled microblogs to cluster words into a limited number of clusters and use the word clusters as features for tweets discovery. Our results indicate that when the number of labeled tweets is 100 or less, the proposed approach is superior to the standard classification based on the bag or words feature representation. We then introduce a human-in-the-loop (HIL) framework for entity identification from noisy web text. Our work explores ways to combine the expressive power of REs, ability of deep learning to learn from large data into a new integrated framework for entity identification from web data. The evaluation on several entity identification problems shows that the proposed framework achieves very high accuracy while requiring only a modest human involvement. We further extend the framework of entity identification to an iterative HIL framework that addresses the entity recognition problem. We particularly investigate how human invest their time when a user is allowed to choose between regex construction and manual labeling. Finally, we address a fundamental problem in the text mining domain, i.e, embedding of rare and out-of-vocabulary (OOV) words, by refining word embedding models and character embedding models in an iterative way. We illustrate the simplicity but effectiveness of our method when applying it to online professional profiles allowing noisy user input. Graph neural networks have been shown great success in the domain of drug design and material sciences, where organic molecules and crystal structures of materials are represented as attributed graphs. A deep learning architecture that is capable of learning from graph nodes and graph edges is crucial for property estimation of molecules. In this dissertation, We propose a simple graph representation for molecules and three neural network architectures that is able to directly learn predictive functions from graphs. We discover that, it is true graph networks are superior than feature-driven algorithms for formation energy prediction. However, the superiority can not be reproduced on band gap prediction. We also discovered that our proposed simple shallow neural networks perform comparably with the state-of-the-art deep neural networks.
Download or read book Leveraging Domain Knowledge in Deep Learning Systems written by Colin M. Van Oort and published by . This book was released on 2021 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning, and the sub-field of deep learning in particular, has experienced an explosion in research interest and practical applications over the past few decades. Deep learning approaches seem to have become the preferred approach in many domains, outpacing the use of more traditional machine learning methods. This transition has also coincided with a shift away from feature engineering based on domain knowledge. Instead, the common deep learning philosophy is to learn relevant features through the combination of expressive models and large datasets. Some have interpreted this paradigm shift as the death of domain knowledge. I argue that domain knowledge is still broadly used in deep learning systems, and even critically important, but where and how domain knowledge is used has evolved. To support this argument I present three recent deep learning applications in disparate domains that each heavily rely on domain knowledge. Based on these three applications I discuss strategies for where and how domain knowledge is being effectively incorporated into newer deep learning systems.
Download or read book Deep Learning for Natural Language Processing written by Karthiek Reddy Bokka and published by Packt Publishing Ltd. This book was released on 2019-06-11 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain 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 Description Applying 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 for If 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.
Download or read book INFORMS Analytics Body of Knowledge written by James J. Cochran and published by John Wiley & Sons. This book was released on 2018-09-21 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Standardizes the definition and framework of analytics #2 on Book Authority’s list of the Best New Analytics Books to Read in 2019 (January 2019) We all want to make a difference. We all want our work to enrich the world. As analytics professionals, we are fortunate - this is our time! We live in a world of pervasive data and ubiquitous, powerful computation. This convergence has inspired and accelerated the development of both analytic techniques and tools and this potential for analytics to have an impact has been a huge call to action for organizations, universities, and governments. This title from Institute for Operations Research and the Management Sciences (INFORMS) represents the perspectives of some of the most respected experts on analytics. Readers with various backgrounds in analytics – from novices to experienced professionals – will benefit from reading about and implementing the concepts and methods covered here. Peer reviewed chapters provide readers with in-depth insights and a better understanding of the dynamic field of analytics The INFORMS Analytics Body of Knowledge documents the core concepts and skills with which an analytics professional should be familiar; establishes a dynamic resource that will be used by practitioners to increase their understanding of analytics; and, presents instructors with a framework for developing academic courses and programs in analytics.
Download or read book Deep Learning Convergence to Big Data Analytics written by Murad Khan and published by Springer. This book was released on 2018-12-30 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
Download or read book Mastering Deep Learning written by Cybellium Ltd and published by Cybellium Ltd. This book was released on with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the Power of Neural Networks for Intelligent Solutions In the landscape of artificial intelligence and machine learning, deep learning stands as a revolutionary force that is shaping the future of technology. "Mastering Deep Learning" is your ultimate guide to comprehending and harnessing the potential of deep neural networks, empowering you to create intelligent solutions that drive innovation. About the Book: As the capabilities of technology expand, deep learning emerges as a transformative approach that unlocks the potential of artificial intelligence. "Mastering Deep Learning" offers a comprehensive exploration of this cutting-edge field—an indispensable toolkit for data scientists, engineers, and enthusiasts. This book caters to both beginners and experienced learners aiming to excel in deep learning concepts, algorithms, and applications. Key Features: Deep Learning Fundamentals: Begin by understanding the core principles of deep learning. Learn about neural networks, activation functions, and backpropagation—the building blocks of the subject. Deep Neural Architectures: Dive into the world of deep neural architectures. Explore techniques for building and designing different types of neural networks, including feedforward, convolutional, and recurrent networks. Training and Optimization: Grasp the art of training deep neural networks. Understand techniques for weight initialization, gradient descent, and optimization algorithms to ensure efficient learning. Natural Language Processing: Explore deep learning applications in natural language processing. Learn how to process and understand text, sentiment analysis, and language generation. Computer Vision: Understand the significance of deep learning in computer vision. Explore techniques for image classification, object detection, and image generation. Reinforcement Learning: Delve into the realm of reinforcement learning. Explore techniques for training agents to interact with environments and make intelligent decisions. Transfer Learning and Pretrained Models: Grasp the power of transfer learning. Learn how to leverage pretrained models and adapt them to new tasks. Real-World Applications: Gain insights into how deep learning is applied across industries. From healthcare to finance, discover the diverse applications of deep neural networks. Why This Book Matters: In an era of rapid technological advancement, mastering deep learning offers a competitive edge. "Mastering Deep Learning" empowers data scientists, engineers, and technology enthusiasts to leverage these cutting-edge concepts, enabling them to create intelligent solutions that drive innovation and redefine possibilities. Unleash the Future of AI: In the landscape of artificial intelligence, deep learning is reshaping technology and innovation. "Mastering Deep Learning" equips you with the knowledge needed to leverage deep neural networks, enabling you to create intelligent solutions that push the boundaries of possibilities. Whether you're a seasoned practitioner or new to the world of deep learning, this book will guide you in building a solid foundation for effective AI-driven solutions. Your journey to mastering deep learning starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Download or read book Towards Green AI written by Sangeeta Srivastava (Researcher in deep learning) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks have not only evolved to produce state-of-the-art artificial intelligence (AI) models, but also generalize well, especially when the network has a large capacity and access to a comparable massive training dataset. These models are computationally and environmentally inefficient. Such research works are known as Red AI. However, it is challenging to develop algorithms that generate cost-effective models for Green AI without sacrificing predictive strength or generality. This dissertation shows how we can effectively integrate domain knowledge, either implicitly or explicitly, into an end-to-end learning pipeline to reduce the training and testing overhead of a neural network. In particular, we exploit the target data and task information to make two types of modifications in the learning pipeline, each of which helps to lower the neural network's run-time costs in unique ways. We re-define the learning task as one or more "simpler" subtask(s) so that the subtask(s) requires fewer parameters and training data. Explicit inclusion of a regularization term in the objective function allows us to limit the exploration space to those that are both relevant and plausible for a given application. We demonstrate the benefits of the above methodologies with the help of three applications with disparate challenges: 1) acoustic event detection, 2) radar classification, and 3) scientific problems based on eigenvalue solvers. Acoustic and radar applications require on-device intelligence and rely on Cortex-M7/M3 microcontroller-based edge devices with limited hardware resources and a low power budget. On the other hand, eigenvalue problems yield memory- and compute-intensive models primarily due to the size of the output layer that grows exponentially with the number of particles in the system. For the acoustic event detector, we exploit unlabeled data from the target domain to constrain knowledge transfer from a large "teacher" model to a smaller "student" model such that the knowledge is relevant to the target problem. This results in a student with orders of magnitude lower static and dynamic memory requirements. For the N + 1 radar classification problem, where the +1-class corresponds to environmental noise, we use an application-specific ontology to decompose the end-to-end learning into two hierarchical subproblems. A more complex model trained to classify the N source classes is invoked occasionally, i.e., only when there is no environmental noise. This conditional invocation makes the resulting model 3.5x more run-time efficient than feature-engineered shallow solutions and gives the best trade-off between accuracy and efficiency. As for the eigenvalue solvers, we decompose the complex regression task of prediction of high-dimensional eigenvector into multiple simpler and parallel subtasks such that the inputs in each subtask have similar physical properties. We learn a parameter-efficient "expert" network for each subtask. Our proposed physics-guided architecture is 150x smaller than the network trained to learn the complex task while being competitive in generalization. Our results also show that domain knowledge helps reduce the amount of supervised data needed for model training.
Download or read book Deep Learning with R Cookbook written by Swarna Gupta and published by Packt Publishing Ltd. This book was released on 2020-02-21 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries Key FeaturesUnderstand the intricacies of R deep learning packages to perform a range of deep learning tasksImplement deep learning techniques and algorithms for real-world use casesExplore various state-of-the-art techniques for fine-tuning neural network modelsBook Description Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques. The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You’ll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you’ll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you’ll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps. By the end of this book, you’ll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems. What you will learnWork with different datasets for image classification using CNNsApply transfer learning to solve complex computer vision problemsUse RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classificationImplement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorizationBuild deep generative models to create photorealistic images using GANs and VAEsUse MXNet to accelerate the training of DL models through distributed computingWho this book is for This deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory.
Download or read book Deep Learning Solutions for High Expertise Domains written by Sean T. Yang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has had significant success in addressing big data's knowledge organization and effective communication problems. However, the technology is difficult to apply to high expertise domains due to limited accessibility to structured data. While data labeling in most deep learning problems only needs common sense, data curation in high expertise domains requires extensive knowledge and experience in these specialized domains. Thus, acquiring large-scale labeled data for high expertise domains is expensive and sometimes difficult. The scientific community is one example of a high expertise application where it is more difficult to apply deep learning due to lack of structured data. We offer solutions to communication challenges caused by an overwhelming number of publications in the scientific community. We demonstrate that scientific figures are a significant channel of communication and they can serve as a tracker of popularity and propagation of the ideas and methods. We next propose networks that automatically identify Central Figures, which are selected from the existing publications and summarize the main contributions of research papers. Central figures can be deployed on online search engines to facilitate a literature review process. We also provide evidence supporting the idea that citation behaviors in individual research documents predicts acceptance decisions, even more so than existing natural language processing models. This bibliography analysis provide additional submission reviewing strategies for publishers or conference coordinators. We extend our studies to broader high-expertise domains based o observations from the exploration of the scientific community. First, we find that application-agnostic ontologies are often invested in these high-expertise domains. These ontologies can be utilized in Hierarchical Multi-label Classification for knowledge organization. We propose a novel framework to address multi-label classification problem and we demonstrate that the proposed model outperforms existing methods by a significant margin. We introduce Global Hierarchical Violation to measure whether the predictions follow the hierarchy constraints. We show that the current benchmarks in hierarchical multi-label classification do not properly represent the problem space and we further introduce a declarative query system to produce customizable datasets along with four benchmarks which better describe the problem. Second, we discover that images in high-expertise domains are often equipped with short text descriptions. We present JECL, which leverages this noisy text description as a source of weak supervision. It simultaneously learns to cluster and joint representations for image-text pairs. We show that JECL outperforms existing multi-view methods on four benchmarks. The learned representations from JECL can be deployed on GraviTIE, an interactive data visualization platform that affords scalability, query, and reproducibility. It allows users to explore large heterogeneous image collections efficiently. This dissertation offers deep learning solutions to challenges arising from low accessibility to structured data in high-expertise domains. The presented analyses within the scientific community provide strategies for researchers to communicate complex ideas efficiently. The proposed methods allow experts to organize knowledge with ontologies and to explore large-scale heterogeneous image collections with more feasibility.
Download or read book Deep Learning in Data Analytics written by Debi Prasanna Acharjya and published by Springer Nature. This book was released on 2021-08-11 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
Download or read book Mining the Talk written by Scott Spangler and published by Pearson Education. This book was released on 2007-07-19 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage Unstructured Data to Become More Competitive, Responsive, and Innovative In Mining the Talk, two leading-edge IBM researchers introduce a revolutionary new approach to unlocking the business value hidden in virtually any form of unstructured data–from word processing documents to websites, emails to instant messages. The authors review the business drivers that have made unstructured data so important–and explain why conventional methods for working with it are inadequate. Then, writing for business professionals–not just data mining specialists–they walk step-by-step through exploring your unstructured data, understanding it, and analyzing it effectively. Next, you’ll put IBM’s techniques to work in five key areas: learning from your customer interactions; hearing the voices of customers when they’re not talking to you; discovering the “collective consciousness” of your own organization; enhancing innovation; and spotting emerging trends. Whatever your organization, Mining the Talk offers you breakthrough opportunities to become more responsive, agile, and competitive. Identify your key information sources and what can be learned about them Discover the underlying structure inherent in your unstructured information Create flexible models that capture both domain knowledge and business objectives Create visual taxonomies: “pictures” of your data and its key interrelationships Combine structured and unstructured information to reveal hidden trends, patterns, and relationships Gain insights from “informal talk” by customers and employees Systematically leverage knowledge from technical literature, patents, and the Web Establish a sustainable process for creating continuing business value from unstructured data Preface xv Acknowledgements xx Chapter 1: Introduction 1 Chapter 2: Mining Customer Interactions 21 Chapter 3: Mining the Voice of the Customer 71 Chapter 4: Mining the Voice of the Employee 93 Chapter 5: Mining to Improve Innovation 111 Chapter 6: Mining to See the Future 133 Chapter 7: Future Applications 163 Appendix: The IBM Unstructured Information Modeler Users Manual 171
Download or read book Mastering Data Science written by Cybellium Ltd and published by Cybellium Ltd. This book was released on with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the Power of Insights from Data Are you ready to embark on a transformative journey into the world of data science? "Mastering Data Science" is your comprehensive guide to unlocking the full potential of data for extracting valuable insights and driving informed decisions. Whether you're an aspiring data scientist looking to enhance your skills or a business leader seeking to leverage data-driven strategies, this book equips you with the knowledge and tools to master the art of data science. Key Features: 1. Dive into Data Science: Immerse yourself in the realm of data science, understanding its core principles, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Visualization: Master the art of data exploration and visualization. Learn how to analyze datasets, uncover patterns, and create compelling visualizations that reveal hidden trends. 3. Statistical Analysis and Hypothesis Testing: Uncover the power of statistical analysis and hypothesis testing. Explore techniques for making data-driven inferences, validating assumptions, and drawing meaningful conclusions. 4. Machine Learning Fundamentals: Delve into machine learning concepts and techniques. Learn about supervised and unsupervised learning, feature engineering, model selection, and evaluation. 5. Predictive Analytics: Discover the realm of predictive analytics. Learn how to build predictive models that forecast future outcomes, enabling proactive decision-making. 6. Natural Language Processing (NLP) and Text Mining: Explore NLP and text mining techniques. Learn how to process and analyze textual data, extract sentiments, and uncover insights from unstructured content. 7. Time Series Analysis: Master time series analysis for modeling sequential data. Learn how to forecast trends, identify seasonality, and make predictions based on temporal patterns. 8. Big Data and Data Wrangling: Dive into big data analytics and data wrangling. Learn how to handle and preprocess large datasets, ensuring data quality and usability. 9. Deep Learning and Neural Networks: Uncover the world of deep learning and neural networks. Learn how to build and train deep learning models for tasks like image recognition and natural language understanding. 10. Real-World Applications: Gain insights into real-world applications of data science across industries. From healthcare to finance, explore how organizations harness data science for strategic decision-making. Who This Book Is For: "Mastering Data Science" is an indispensable resource for aspiring data scientists, analysts, and business professionals who want to excel in extracting insights from data. Whether you're new to data science or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data for innovation. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Download or read book Data Science written by Gyanendra K. Verma and published by Springer Nature. This book was released on 2021-08-19 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.
Download or read book Deep Learning Essentials written by Anurag Bhardwaj and published by Packt Publishing Ltd. This book was released on 2018-01-30 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.
Download or read book Handbook of Geospatial Artificial Intelligence written by Song Gao and published by CRC Press. This book was released on 2023-12-29 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography. Features Provides systematic introductions and discussions of GeoAI theory, methods, technologies, applications, and future perspectives Covers a wide range of GeoAI applications and case studies in practice Offers supplementary materials such as data, programming code, tools, and case studies Discusses the recent developments of GeoAI methods and tools Includes contributions written by top experts in cutting-edge GeoAI topics This book is intended for upper-level undergraduate and graduate students from different disciplines and those taking GIS courses in geography or computer sciences as well as software engineers, geospatial industry engineers, GIS professionals in non-governmental organizations, and federal/state agencies who use GIS and want to learn more about GeoAI advances and applications.
Download or read book Cognitive Computing and Big Data Analytics written by Judith S. Hurwitz and published by John Wiley & Sons. This book was released on 2015-02-12 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data. This book helps technologists understand cognitive computing's underlying technologies, from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches based on accumulated evidence, rather than reprogramming. Detailed case examples from the financial, healthcare, and manufacturing walk readers step-by-step through the design and testing of cognitive systems, and expert perspectives from organizations such as Cleveland Clinic, Memorial Sloan-Kettering, as well as commercial vendors that are creating solutions. These organizations provide insight into the real-world implementation of cognitive computing systems. The IBM Watson cognitive computing platform is described in a detailed chapter because of its significance in helping to define this emerging market. In addition, the book includes implementations of emerging projects from Qualcomm, Hitachi, Google and Amazon. Today's cognitive computing solutions build on established concepts from artificial intelligence, natural language processing, ontologies, and leverage advances in big data management and analytics. They foreshadow an intelligent infrastructure that enables a new generation of customer and context-aware smart applications in all industries. Cognitive Computing is a comprehensive guide to the subject, providing both the theoretical and practical guidance technologists need. Discover how cognitive computing evolved from promise to reality Learn the elements that make up a cognitive computing system Understand the groundbreaking hardware and software technologies behind cognitive computing Learn to evaluate your own application portfolio to find the best candidates for pilot projects Leverage cognitive computing capabilities to transform the organization Cognitive systems are rightly being hailed as the new era of computing. Learn how these technologies enable emerging firms to compete with entrenched giants, and forward-thinking established firms to disrupt their industries. Professionals who currently work with big data and analytics will see how cognitive computing builds on their foundation, and creates new opportunities. Cognitive Computing provides complete guidance to this new level of human-machine interaction.
Download or read book Deep Learning at Scale written by Suneeta Mall and published by "O'Reilly Media, Inc.". This book was released on 2024-06-18 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of: How data flows through the deep-learning network and the role the computation graphs play in building your model How accelerated computing speeds up your training and how best you can utilize the resources at your disposal How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training How to expedite the training lifecycle and streamline your feedback loop to iterate model development A set of data tricks and techniques and how to apply them to scale your training model How to select the right tools and techniques for your deep-learning project Options for managing the compute infrastructure when running at scale
