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Book Synopsis Hardware-Aware Probabilistic Machine Learning Models by : Laura Isabel Galindez Olascoaga
Download or read book Hardware-Aware Probabilistic Machine Learning Models written by Laura Isabel Galindez Olascoaga and published by Springer Nature. This book was released on 2021-05-19 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover. The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.
Book Synopsis IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning by : Joao Gama
Download or read book IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning written by Joao Gama and published by Springer Nature. This book was released on 2021-01-09 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.
Book Synopsis Probabilistic Machine Learning by : Kevin P. Murphy
Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Book Synopsis Probabilistic Machine Learning by : Kevin P. Murphy
Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2023-08-15 with total page 1352 pages. Available in PDF, EPUB and Kindle. Book excerpt: An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning. Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment
Book Synopsis Efficient Execution of Irregular Dataflow Graphs by : Nimish Shah
Download or read book Efficient Execution of Irregular Dataflow Graphs written by Nimish Shah and published by Springer Nature. This book was released on 2023-08-14 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the acceleration of emerging irregular sparse workloads, posed by novel artificial intelligent (AI) models and sparse linear algebra. Specifically, the book outlines several co-optimized hardware-software solutions for a highly promising class of emerging sparse AI models called Probabilistic Circuit (PC) and a similar sparse matrix workload for triangular linear systems (SpTRSV). The authors describe optimizations for the entire stack, targeting applications, compilation, hardware architecture and silicon implementation, resulting in orders of magnitude higher performance and energy-efficiency compared to the existing state-of-the-art solutions. Thus, this book provides important building blocks for the upcoming generation of edge AI platforms.
Book Synopsis Statistical Relational Artificial Intelligence by : Luc De Raedt
Download or read book Statistical Relational Artificial Intelligence written by Luc De Raedt and published by Morgan & Claypool Publishers. This book was released on 2016-03-24 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.
Book Synopsis Advances in Intelligent Data Analysis XVIII by : Michael R. Berthold
Download or read book Advances in Intelligent Data Analysis XVIII written by Michael R. Berthold and published by Springer Nature. This book was released on 2020-04-22 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.
Book Synopsis Probabilistic Deep Learning by : Oliver Duerr
Download or read book Probabilistic Deep Learning written by Oliver Duerr and published by Manning Publications. This book was released on 2020-11-10 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. Summary Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pulling it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work. About the book Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. What's inside Explore maximum likelihood and the statistical basis of deep learning Discover probabilistic models that can indicate possible outcomes Learn to use normalizing flows for modeling and generating complex distributions Use Bayesian neural networks to access the uncertainty in the model About the reader For experienced machine learning developers. About the author Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. Elvis Murina is a data scientist. Table of Contents PART 1 - BASICS OF DEEP LEARNING 1 Introduction to probabilistic deep learning 2 Neural network architectures 3 Principles of curve fitting PART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS 4 Building loss functions with the likelihood approach 5 Probabilistic deep learning models with TensorFlow Probability 6 Probabilistic deep learning models in the wild PART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS 7 Bayesian learning 8 Bayesian neural networks
Book Synopsis Machine Learning by : Kevin P. Murphy
Download or read book Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2012-08-24 with total page 1102 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Book Synopsis An Introduction to Lifted Probabilistic Inference by : Guy Van den Broeck
Download or read book An Introduction to Lifted Probabilistic Inference written by Guy Van den Broeck and published by MIT Press. This book was released on 2021-08-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.
Download or read book Fundamentals written by Katharina Morik and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-12-31 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is part of Artificial Intelligence since its beginning. Certainly, not learning would only allow the perfect being to show intelligent behavior. All others, be it humans or machines, need to learn in order to enhance their capabilities. In the eighties of the last century, learning from examples and modeling human learning strategies have been investigated in concert. The formal statistical basis of many learning methods has been put forward later on and is still an integral part of machine learning. Neural networks have always been in the toolbox of methods. Integrating all the pre-processing, exploitation of kernel functions, and transformation steps of a machine learning process into the architecture of a deep neural network increased the performance of this model type considerably. Modern machine learning is challenged on the one hand by the amount of data and on the other hand by the demand of real-time inference. This leads to an interest in computing architectures and modern processors. For a long time, the machine learning research could take the von-Neumann architecture for granted. All algorithms were designed for the classical CPU. Issues of implementation on a particular architecture have been ignored. This is no longer possible. The time for independently investigating machine learning and computational architecture is over. Computing architecture has experienced a similarly rampant development from mainframe or personal computers in the last century to now very large compute clusters on the one hand and ubiquitous computing of embedded systems in the Internet of Things on the other hand. Cyber-physical systems’ sensors produce a huge amount of streaming data which need to be stored and analyzed. Their actuators need to react in real-time. This clearly establishes a close connection with machine learning. Cyber-physical systems and systems in the Internet of Things consist of diverse components, heterogeneous both in hard- and software. Modern multi-core systems, graphic processors, memory technologies and hardware-software codesign offer opportunities for better implementations of machine learning models. Machine learning and embedded systems together now form a field of research which tackles leading edge problems in machine learning, algorithm engineering, and embedded systems. Machine learning today needs to make the resource demands of learning and inference meet the resource constraints of used computer architecture and platforms. A large variety of algorithms for the same learning method and, moreover, diverse implementations of an algorithm for particular computing architectures optimize learning with respect to resource efficiency while keeping some guarantees of accuracy. The trade-off between a decreased energy consumption and an increased error rate, to just give an example, needs to be theoretically shown for training a model and the model inference. Pruning and quantization are ways of reducing the resource requirements by either compressing or approximating the model. In addition to memory and energy consumption, timeliness is an important issue, since many embedded systems are integrated into large products that interact with the physical world. If the results are delivered too late, they may have become useless. As a result, real-time guarantees are needed for such systems. To efficiently utilize the available resources, e.g., processing power, memory, and accelerators, with respect to response time, energy consumption, and power dissipation, different scheduling algorithms and resource management strategies need to be developed. This book series addresses machine learning under resource constraints as well as the application of the described methods in various domains of science and engineering. Turning big data into smart data requires many steps of data analysis: methods for extracting and selecting features, filtering and cleaning the data, joining heterogeneous sources, aggregating the data, and learning predictions need to scale up. The algorithms are challenged on the one hand by high-throughput data, gigantic data sets like in astrophysics, on the other hand by high dimensions like in genetic data. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are applied to program executions in order to save resources. The three books will have the following subtopics: Volume 1: Machine Learning under Resource Constraints - Fundamentals Volume 2: Machine Learning and Physics under Resource Constraints - Discovery Volume 3: Machine Learning under Resource Constraints - Applications Volume 1 establishes the foundations of this new field (Machine Learning under Resource Constraints). It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.
Book Synopsis Computational Intelligence for Green Cloud Computing and Digital Waste Management by : Kumar, K. Dinesh
Download or read book Computational Intelligence for Green Cloud Computing and Digital Waste Management written by Kumar, K. Dinesh and published by IGI Global. This book was released on 2024-02-27 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the digital age, the relentless growth of data centers and cloud computing has given rise to a pressing dilemma. The power consumption of these facilities is spiraling out of control, emitting massive amounts of carbon dioxide, and contributing to the ever-increasing threat of global warming. Studies show that data centers alone are responsible for nearly eighty million metric tons of CO2 emissions worldwide, and this figure is poised to skyrocket to a staggering 8000 TWh by 2030 unless we revolutionize our approach to computing resource management. The root of this problem lies in inefficient resource allocation within cloud environments, as service providers often over-provision computing resources to avoid Service Level Agreement (SLA) violations, leading to both underutilization of resources and a significant increase in energy consumption. Computational Intelligence for Green Cloud Computing and Digital Waste Management stands as a beacon of hope in the face of the environmental and technological challenges we face. It introduces the concept of green computing, dedicated to creating an eco-friendly computing environment. The book explores innovative, intelligent resource management methods that can significantly reduce the power consumption of data centers. From machine learning and deep learning solutions to green virtualization technologies, this comprehensive guide explores innovative approaches to address the pressing challenges of green computing. Whether you are an educator teaching about green computing, an environmentalist seeking sustainability solutions, an industry professional navigating the digital landscape, a resolute researcher, or simply someone intrigued by the intersection of technology and sustainability, this book offers an indispensable resource.
Book Synopsis Machine Learning and Probabilistic Graphical Models for Decision Support Systems by : Kim Phuc Tran
Download or read book Machine Learning and Probabilistic Graphical Models for Decision Support Systems written by Kim Phuc Tran and published by CRC Press. This book was released on 2022-10-13 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Book Synopsis Embedded Computer Systems: Architectures, Modeling, and Simulation by : Alex Orailoglu
Download or read book Embedded Computer Systems: Architectures, Modeling, and Simulation written by Alex Orailoglu and published by Springer Nature. This book was released on 2022-04-26 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 21st International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021, which took place in July 2021. Due to COVID-19 pandemic the conference was held virtually. The 17 full papers presented in this volume were carefully reviewed and selected from 45 submissions. The papers are organized in topics as follows: simulation and design space exploration; the 3Cs - Cache, Cluster and Cloud; heterogeneous SoC; novel CPU architectures and applications; dataflow; innovative architectures and tools for security; next generation computing; insights from negative results.
Book Synopsis Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention by : Luping Zhou
Download or read book Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention written by Luping Zhou and published by Springer Nature. This book was released on 2019-11-20 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications in medical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection.
Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Book Synopsis Machine Learning For Beginners by : Chris Neil
Download or read book Machine Learning For Beginners written by Chris Neil and published by . This book was released on 2020-12-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to understand machine learning? How it works and how is correlated to artificial intelligence and deep learning? If yes, then keep reading... Machine Learning is based on mathematics, specifically statistics. It is a probabilistic discipline that began in the 1950s. Despite initial enthusiasm, research and development in Machine Learning languished for over 30 years, suffering from twin ills of a lack of data to work with and computers that were too slow to effectively work with what data they had. It is no accident Machine Learning is coming into its own over the last 10 years. Until we began creating and storing massive amounts of data about our world, ML was mostly an idea in the minds of statisticians. And until computers reached a level of speed and power where these massive data sets could be ingested in a reasonable amount of time, the revolution couldn't happen. But as we digitize information about our world and ourselves, and computers continue to increase in speed and capacity exponentially, the ability for Machine Learning to learn from our data grows in depth and accuracy. Looking to the future, we can see only more and more data collection about our world, faster computer chips, and data transfer, and more avenues for ML to develop in, to grow and learn, and to serve humanity. When most people think of machine learning, they either have no idea what it is, or they automatically think about artificial intelligence in the form of a robotic species that rivals humans. While these fascinating subspecies may one day exist as the result of machine learning developments, right now the primary focus is on how machine learning programs can become excellent at very specific tasks. Most machine learning technology is developed in such a way that it is excellent at performing one or, at most, two tasks. By focusing entire technology on one single task, they can ensure that it runs that task perfectly, and that it does not get confused between the tasks that it is trying to accomplish. While simple computing software like the one that runs your computer can easily run multiple programs at once with little chance of crashing, the technology that is used to run machine learning technology is far more complex. As researchers study it, they strive to keep the algorithms mostly separate, or specifically focused on completing just one goal, on minimizing room for error. It is likely that as we become more familiar with machine learning technology and more educated in the algorithms, we will start to see more and more machines completing multiple tasks, rather than just one. At this point, that is the long-term goal for many scientists who want to see these machines becoming more efficient, and requiring less hardware. After all, the hardware used to run some of these machines is not always the greenest technology, so the fewer hardware casings that technology needs to be stored in, the less of a footprint the technology sector will have on the planet. This book aims to educate you on the truth about machine learning. This book gives a comprehensive guide on the following: What is Machine Learning? Machine Learning Categories Sectors and Industries that use Machine Learning Fundamental Algorithms Regression Analysis Benefits of Machine Learning Deep Learning Deep Neural Network Big Data Analytics Big Data Analysis Tools How Companies Use Big Data Data Mining and Applications ... AND MORE!!! What are you waiting for? Click buy now!!!!!
