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 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 Hands On Deep Learning Architectures with Python written by Yuxi (Hayden) Liu and published by Packt Publishing Ltd. This book was released on 2019-04-30 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concepts, tools, and techniques to explore deep learning architectures and methodologies Key FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book

Download or read book Advances in Scalable and Intelligent Geospatial Analytics written by Surya S Durbha and published by CRC Press. This book was released on 2023-05-12 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geospatial data acquisition and analysis techniques have experienced tremendous growth in the last few years, providing an opportunity to solve previously unsolved environmental- and natural resource-related problems. However, a variety of challenges are encountered in processing the highly voluminous geospatial data in a scalable and efficient manner. Technological advancements in high-performance computing, computer vision, and big data analytics are enabling the processing of big geospatial data in an efficient and timely manner. Many geospatial communities have already adopted these techniques in multidisciplinary geospatial applications around the world. This book is a single source that offers a comprehensive overview of the state of the art and future developments in this domain. FEATURES Demonstrates the recent advances in geospatial analytics tools, technologies, and algorithms Provides insight and direction to the geospatial community regarding the future trends in scalable and intelligent geospatial analytics Exhibits recent geospatial applications and demonstrates innovative ways to use big geospatial data to address various domain-specific, real-world problems Recognizes the analytical and computational challenges posed and opportunities provided by the increased volume, velocity, and veracity of geospatial data This book is beneficial to graduate and postgraduate students, academicians, research scholars, working professionals, industry experts, and government research agencies working in the geospatial domain, where GIS and remote sensing are used for a variety of purposes. Readers will gain insights into the emerging trends on scalable geospatial data analytics.

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 The Deep Learning Workshop written by Mirza Rahim Baig and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Key Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition Study the architecture of different neural networks Book Description Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras. What you will learn Understand how deep learning, machine learning, and artificial intelligence are different Develop multilayer deep neural networks with TensorFlow Implement deep neural networks for multiclass classification using Keras Train CNN models for image recognition Handle sequence data and use it in conjunction with RNNs Build a GAN to generate high-quality synthesized images Who this book is for If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.

Download or read book Deep Learning written by Robert Hack and published by . This book was released on 2020-04-05 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Everything You Need to Know About Deep LearningDo you want to know all about Deep Learning?Wondering what you need to get started with Deep Learning?You Are 1-Click Away From Knowing All About Deep Learning.Hello! Welcome to this guide to "The Ultimate Beginner's Guide To Artificial Intelligence And Neural Networks" An understanding of deep learning begins with a precise definition of terms. Otherwise, you have a hard time separating the media hype from the realities of what deep learning can actually provide. Deep learning is part of both AI and machine learning. To understand deep learning, you must begin at the outside - that is, you start with AI, and then work your way through machine learning, and then finally define deep learning. This book would help you through this process. Why study Deep Learning Has best-in-class performance on problems that significantly outperforms other solutions in multiple domains. This includes speech, language, vision, playing games like Go etc. This isn't by a little bit, but by a significant amount. Reduces the need for feature engineering, one of the most time-consuming parts of machine learning practice. Is an architecture that can be adapted to new problems relatively easily e.g. Vision, time series, language etc., are using techniques like convolutional neural networks, recurrent neural networks, long short-term memory etc. Feature engineering can be automatically executed inside Deep Learning model Can solve complex problems flexible to be adapted to new challenge in the future (or transfer learning can be easily applied) High automation. Deep learning library (Tensorflow, keras, or MATLAB...) can help users build a deep learning model in seconds (without the need of deep understanding) More precisely, the book will teach you: Introduction to Deep Learning History of Deep Learning Conceptual foundations Neural Networks: The Building Blocks of Deep Learning training deep networks Convolutional and Recurrent Neural Networks Learning Functions The Future of Deep Learning And so much more Frequently Asked QuestionsQ: Do I need special software or hardware to read eBooks? A: All you need is your PC, laptop or hand held device and the free Reader software. We offer eBooks in three different formats: PDF download, EPUB download and Online Reader. Our Online Reader requires no software other than an internet browser. For downloading, we will provide you with a link to download the appropriate Reader software free of charge when you make a purchase. Q: How to buy kindle eBook? A: You can purchase Kindle books at any time using a web browser. Visit Kindle Store to start browsing. To purchase Kindle books using your reading app: Tap the Store tab or Shop in Kindle Store. Browse or search for the Kindle titles you want to read. Select Buy Now. So, what are you waiting for? Buy now to join the millions of people already learning about Deep Learning!

Download or read book The Machine Learning Solutions Architect Handbook written by David Ping and published by Packt Publishing Ltd. This book was released on 2022-01-21 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building, and model training Learn how to implement bias detection, privacy, and explainability in ML model development Book DescriptionWhen equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional. What you will learn Apply ML methodologies to solve business problems Design a practical enterprise ML platform architecture Implement MLOps for ML workflow automation Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using an AI service and a custom ML model Use AWS services to detect data and model bias and explain models Who this book is for This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.

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 Methods and Techniques in Deep Learning written by Avik Santra and published by John Wiley & Sons. This book was released on 2022-11-21 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods and Techniques in Deep Learning Introduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmWave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book: Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmWave radar sensors Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization and tracking, in-cabin automotive occupancy sensing Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.

Download or read book Machine Learning Methods for Planning written by Steven Minton and published by Morgan Kaufmann Publishers. This book was released on 1993 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on planning systems has shown that domain knowledge is crucial for effectively coping with complex, changing environments. Unfortunately, acquiring and incorporating the necessary domain knowledge can be a significant problem when building a practical planning system. The knowledge engineering process is typically time-consuming and expensive. Furthermore, if a human expert is not available it may be extremely difficult to obtain the necessary knowledge. One solution is for a system to automatically acquire domain-specific knowledge through learning. The idea of a planning system that can improve its performance with experience is very attractive. Furthermore, advances in machine learning have provided a deeper understanding of learning mechanisms relevant to acquiring such knowledge. For this reason, there is a great deal of interest in this area of artificial intelligence. This book brings together, in one volume, a set of chapters from the primary researchers in the field, presenting a picture of its current state and its likely areas for application. The chapters describe a variety of learning methods

Download or read book Deep Learning with R for Beginners written by Mark Hodnett and published by Packt Publishing Ltd. This book was released on 2019-05-20 with total page 605 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key FeaturesGet to grips with the fundamentals of deep learning and neural networksUse R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processingImplement effective deep learning systems in R with the help of end-to-end projectsBook Description Deep learning finds practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The book will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects. This Learning Path includes content from the following Packt products: R Deep Learning Essentials - Second Edition by Joshua F. Wiley and Mark HodnettR Deep Learning Projects by Yuxi (Hayden) Liu and Pablo MaldonadoWhat you will learnImplement credit card fraud detection with autoencodersTrain neural networks to perform handwritten digit recognition using MXNetReconstruct images using variational autoencodersExplore the applications of autoencoder neural networks in clustering and dimensionality reductionCreate natural language processing (NLP) models using Keras and TensorFlow in RPrevent models from overfitting the data to improve generalizabilityBuild shallow neural network prediction modelsWho this book is for This Learning Path is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. A fundamental understanding of R programming and familiarity with the basic concepts of deep learning are necessary to get the most out of this Learning Path.

Download or read book Prediction and Analysis for Knowledge Representation and Machine Learning written by Avadhesh Kumar and published by CRC Press. This book was released on 2022-01-31 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system’s perceived data, assisting scientists in the generation or refinement of current models. Machine learning is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to machine learning algorithms. It has a significant impact on comprehending difficult scientific problems. Prediction and Analysis for Knowledge Representation and Machine Learning demonstrates various knowledge representation and machine learning methodologies and architectures that will be active in the research field. The approaches are reviewed with real-life examples from a wide range of research topics. An understanding of a number of techniques and algorithms that are implemented in knowledge representation in machine learning is available through the book’s website. Features: Examines the representational adequacy of needed knowledge representation Manipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original information Improves inferential and acquisition efficiency by applying automatic methods to acquire new knowledge Covers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technology Describes the ideas of knowledge representation and related technologies, as well as their applications, in order to help humankind become better and smarter This book serves as a reference book for researchers and practitioners who are working in the field of information technology and computer science in knowledge representation and machine learning for both basic and advanced concepts. Nowadays, it has become essential to develop adaptive, robust, scalable, and reliable applications and also design solutions for day-to-day problems. The edited book will be helpful for industry people and will also help beginners as well as high-level users for learning the latest things, which include both basic and advanced concepts.

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 Efficient Transfer Learning for Heterogeneous Machine Learning Domains written by Zhuangdi Zhu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in deep machine learning hinge on a large amount of labeled data. Such heavy dependence on supervision data impedes the broader application of deep learning in more practical scenarios, where data annotation and labeling can be expensive (e.g. high-frequency trading) or even dangerous (e.g. training autonomous-driving models.) Transfer Learning (TL), equivalently referred to as knowledge transfer, is an effective strategy to confront such challenges. TL, by its definition, distills the external knowledge from relevant domains into the target learning domain, hence requiring fewer supervision resources than learning-from-scratch. TL is beneficial for learning tasks for which the supervision data is limited or even unavailable. It is also an essential property to realize Generalized Artificial Intelligence. In this thesis, we propose sample-efficient TL approaches using limited, sometimes unreliable resources. We take a deep look into the setting of Reinforcement Learning (RL) and Supervised Learning, and derive solutions for the two domains respectively. Especially, for RL, we focus on a problem setting called imitation learning, where the supervision from the environment is either non-available or scarcely provided, and the learning agent must transfer knowledge from exterior resources, such as demonstration examples of a previously trained expert, to learn a good policy. For supervised learning, we consider a distributed machine learning scheme called Federated Learning (FL), which is a more challenging scenario than traditional machine learning, since the training data is distributed and non-sharable during the learning process. Under this distributed setting, it is imperative to enable TL among distributed learning clients to reach a satisfiable generalization performance. We prove by both theoretical support and extensive experiments that our proposed algorithms can facilitate the machine learning process with knowledge transfer to achieve higher asymptotic performance, in a principled and more efficient manner than the prior arts.

Download or read book R Deep Learning Projects written by Yuxi (Hayden) Liu and published by Packt Publishing Ltd. This book was released on 2018-02-22 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: 5 real-world projects to help you master deep learning concepts Key Features Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices Book Description R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting. What you will learn Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Apply neural networks to perform handwritten digit recognition using MXNet Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification -Implement credit card fraud detection with Autoencoders Master reconstructing images using variational autoencoders Wade through sentiment analysis from movie reviews Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction Who this book is for Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.

Download or read book Deep Learning Systems written by Andres Rodriguez and published by Springer Nature. This book was released on 2022-05-31 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.