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End To End Modeling
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Book Synopsis Machine Learning Design Patterns by : Valliappa Lakshmanan
Download or read book Machine Learning Design Patterns written by Valliappa Lakshmanan and published by O'Reilly Media. This book was released on 2020-10-15 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
Book Synopsis End-to-End M&A Process Design by : Thorsten Feix
Download or read book End-to-End M&A Process Design written by Thorsten Feix and published by Springer Nature. This book was released on 2020-07-07 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: The textbook provides a holistic M&A reference model for capturing value and transaction rational in dynamic eco-systems in the 2020s. The digitalized End-to-End M&A Process Design applies five process modules. It fosters the full-scope of digital tools and describes how it could be applied for shaping business model innovations and revitalize corporate portfolios and vice versa.This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.
Book Synopsis Learning Deep Architectures for AI by : Yoshua Bengio
Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
Download or read book ARIS Design Platform written by Rob Davis and published by Springer Science & Business Media. This book was released on 2008-09-15 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following on from Rob Davis’ successful introductory book, ARIS Design Platform: Getting Started with BPM, Rob now covers in detail some of the more advanced concepts of using ARIS Business Architect. This is a practical ‘how-to’ guide and contains tips, techniques and short cuts gained from practical experience and explains clearly how to use ARIS and why ARIS is a powerful tool for process modeling. Advanced concepts such as the following are presented in this reader-friendly and concise guide: - Matrix editor, - Find and query, - Model generation, - Method filters and method changes, - Templates and fonts, - Reports and semantic checks, - Macros, - Transformations, - Database administration, - User management. This easy-to-follow advanced text is a must have guide and reference for all users who want to increase their ARIS skills, and for those who need to undertake advanced model and database management.
Book Synopsis Deep Learning for Robot Perception and Cognition by : Alexandros Iosifidis
Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Book Synopsis Business Process Modelling with ARIS by : Rob Davis
Download or read book Business Process Modelling with ARIS written by Rob Davis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book describes the key operations of ARIS Toolset - the market leading Business Process Modelling Tool. Based on his experience of using ARIS in British Telecommunications plc, the author describes practical ways of using the tool. Using screen shots and plenty of practical examples, Rob Davis shows how ARIS can be used to model business processes. Throughout the book Davis provides readers with tips and short-cuts, enabling users to start modelling quickly and effectively. He also provides insights into the ARIS concepts, and tells readers about the benefits and trade-offs of using the tool in alternative ways. Unlike other books, this practical guide tackles issues found in real projects.
Book Synopsis Practical Machine Learning for Computer Vision by : Valliappa Lakshmanan
Download or read book Practical Machine Learning for Computer Vision written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2021-07-21 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
Book Synopsis Advanced Methods, Techniques, and Applications in Modeling and Simulation by : Jong-Hyun Kim
Download or read book Advanced Methods, Techniques, and Applications in Modeling and Simulation written by Jong-Hyun Kim and published by Springer Science & Business Media. This book was released on 2012-10-04 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a compilation of research accomplishments in the fields of modeling, simulation, and their applications, as presented at AsiaSim 2011 (Asia Simulation Conference 2011). The conference, held in Seoul, Korea, November 16–18, was organized by ASIASIM (Federation of Asian Simulation Societies), KSS (Korea Society for Simulation), CASS (Chinese Association for System Simulation), and JSST (Japan Society for Simulation Technology). AsiaSim 2011 provided a forum for scientists, academicians, and professionals from the Asia-Pacific region and other parts of the world to share their latest exciting research findings in modeling and simulation methodologies, techniques, and their tools and applications in military, communication network, industry, and general engineering problems.
Book Synopsis Handbook of Research on Methodologies and Applications of Supercomputing by : Veljko Milutinovic
Download or read book Handbook of Research on Methodologies and Applications of Supercomputing written by Veljko Milutinovic and published by Engineering Science Reference. This book was released on 2021-02-19 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book offers a variety of perspectives and summarize the advances of control flow and data flow super computing, shedding light on selected emerging big data applications needing high acceleration and/or low power"--
Book Synopsis Artificial Intelligence-based Internet of Things Systems by : Souvik Pal
Download or read book Artificial Intelligence-based Internet of Things Systems written by Souvik Pal and published by Springer Nature. This book was released on 2022-01-11 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book discusses the evolution of future generation technologies through Internet of Things (IoT) in the scope of Artificial Intelligence (AI). The main focus of this volume is to bring all the related technologies in a single platform, so that undergraduate and postgraduate students, researchers, academicians, and industry people can easily understand the AI algorithms, machine learning algorithms, and learning analytics in IoT-enabled technologies. This book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable AI-enabled IoT ecosystem and to implement cyber-physical pervasive infrastructure solutions. This book brings together some of the top IoT-enabled AI experts throughout the world who contribute their knowledge regarding different IoT-based technology aspects.
Book Synopsis Recent Advances in Modeling and Simulation Tools for Communication Networks and Services by : Nejat Ince
Download or read book Recent Advances in Modeling and Simulation Tools for Communication Networks and Services written by Nejat Ince and published by Springer Science & Business Media. This book was released on 2007-09-20 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains a selection of papers presented at a symposium organized under the aegis of COST Telecommunications Action 285. COST (European Cooperation in the field of Scientific and Technical Research) is a framework for scientific and technical cooperation, allowing the coordination of national research on a European level. Action 285 sought to enhance existing tools and develop new modeling and simulation tools.
Book Synopsis Machine Learning for Algorithmic Trading by : Stefan Jansen
Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Book Synopsis Grokking Deep Learning by : Andrew W. Trask
Download or read book Grokking Deep Learning written by Andrew W. Trask and published by Simon and Schuster. This book was released on 2019-01-23 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
Book Synopsis Embedded and Ubiquitous Computing - EUC 2005 by : Laurence T. Yang
Download or read book Embedded and Ubiquitous Computing - EUC 2005 written by Laurence T. Yang and published by Springer Science & Business Media. This book was released on 2005-11-24 with total page 1226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to the proceedings of the 2005 IFIP International Conference on - bedded and Ubiquitous Computing (EUC 2005), which was held in Nagasaki, Japan, December 6–9, 2005. Embedded and ubiquitous computing is emerging rapidly as an exciting new paradigm to provide computing and communication services all the time, - erywhere. Its systems are now pervading every aspect of life to the point that they are hidden inside various appliances or can be worn unobtrusively as part of clothing and jewelry. This emergence is a natural outcome of research and technological advances in embedded systems, pervasive computing and c- munications, wireless networks, mobile computing, distributed computing and agent technologies, etc. Its tremendous impact on academics, industry, gove- ment, and daily life can be compared to that of electric motors over the past century, in fact it but promises to revolutionize life much more profoundly than elevators, electric motors or even personal computers. The EUC 2005 conference provided a forum for engineers and scientists in academia, industry, and government to address profound issues including te- nical challenges, safety, and social, legal, political, and economic issues, and to present and discuss their ideas, results, work in progress, and experience on all aspects of embedded and ubiquitous computing.
Book Synopsis Hierarchical Modeling of Energy Systems by : Nikolai I. Voropai
Download or read book Hierarchical Modeling of Energy Systems written by Nikolai I. Voropai and published by Elsevier. This book was released on 2023-08-03 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hierarchical Modeling of Energy Systems presents a detailed methodology for hierarchical modeling of large-scale complex systems with a focus on energy systems and their expansion planning and control. General methodological principles of hierarchical modeling are analyzed, and based on this analysis, a generalized technology for the hierarchical approach is presented. The mathematical foundations of decomposition and bi-level programming, as well as the possibility of using information technologies are also considered. The theoretical propositions are demonstrated by numerous hierarchical modeling examples aimed at planning the development of the energy sector and expansion of energy systems, analyzing, and optimizing these systems, and controlling their operation. In addition, codes and sample simulations are included throughout. This is an invaluable guide for researchers, engineers, and other specialists involved in the development, control and management of energy systems, while the summary of fundamental principles and concepts in energy modeling makes this an accessible learning tool for graduate students on any course involving energy systems or energy modeling. - Summarizes hierarchical modeling principles and methods - Critically evaluates all energy systems including electric power systems, heat supply systems, gas, and coal supply systems, integrated and cogeneration systems, its interrelations and more - Examines expansion planning, development and operation, control and management of energy systems - Provides a detailed mathematical descriptions of models, computation algorithms, and optimization problems
Book Synopsis Benchmarking, Measuring, and Optimizing by : Wanling Gao
Download or read book Benchmarking, Measuring, and Optimizing written by Wanling Gao and published by Springer Nature. This book was released on 2020-06-09 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019, held in Denver, CO, USA, in November 2019. The 20 full papers and 11 short papers presented were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections named: Best Paper Session; AI Challenges on Cambircon using AIBenc; AI Challenges on RISC-V using AIBench; AI Challenges on X86 using AIBench; AI Challenges on 3D Face Recognition using AIBench; Benchmark; AI and Edge; Big Data; Datacenter; Performance Analysis; Scientific Computing.
Book Synopsis Deep Reinforcement Learning by : Aske Plaat
Download or read book Deep Reinforcement Learning written by Aske Plaat and published by Springer Nature. This book was released on 2022-06-10 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.
