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Author: Moritz Hardt
Publisher: Princeton University Press
ISBN: 0691233721
Category : Computers
Languages : en
Pages : 321
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Book Description
An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers
Author: Moritz Hardt
Publisher: Princeton University Press
ISBN: 0691233721
Category : Computers
Languages : en
Pages : 321
Get Book
Book Description
An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers
Author: Moritz Hardt
Publisher: Learningbooks
ISBN: 9789732348086
Category : Computers
Languages : en
Pages : 0
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Book Description
Dive into the captivating world of artificial intelligence and data-driven innovation with "Patterns, Predictions, and Actions: A Story about Machine Learning" by acclaimed authors Moritz Hardt and Benjamin Recht. This enthralling narrative unfolds like a carefully crafted algorithm, weaving together the threads of cutting-edge technology, human ingenuity, and the limitless possibilities of machine learning. Embark on a journey that unravels the intricate patterns hidden within vast datasets, as Hardt and Recht skillfully guide you through the labyrinth of algorithms and models. Immerse yourself in the language of data science, where every line of code tells a story, and every prediction holds the key to unlocking unprecedented insights. From regression analysis to deep neural networks, this book explores the diverse landscape of machine learning, offering readers a comprehensive understanding of the tools shaping the future. As you turn the pages, you'll witness the power of predictive analytics as it transcends industries, from finance to healthcare, and transforms the way we approach complex problems. The authors illuminate the synergy between man and machine, emphasizing how collaborative efforts between humans and algorithms can usher in a new era of technological advancement and societal progress. "Patterns, Predictions, and Actions" is not merely a book; it's a roadmap for the curious minds seeking to decipher the intricate dance between data and decisions. With each chapter, you'll discover how machine learning algorithms unravel patterns in chaos, predict future trends with uncanny accuracy, and ultimately empower us to take decisive actions that shape the world around us. This literary masterpiece is a treasure trove of knowledge for both the seasoned data scientist and the curious novice. Whether you're fascinated by the mathematical intricacies of machine learning or intrigued by its real-world applications, this book offers a rare blend of technical expertise and storytelling prowess. Uncover the secrets of machine learning, demystify the algorithms driving innovation, and embark on a journey that explores the intersection of human intuition and artificial intelligence. "Patterns, Predictions, and Actions" invites you to envision a future where the marriage of data and decision-making transforms not just industries, but the very fabric of our existence. Immerse yourself in this captivating narrative, and let the algorithms guide you through a story that is as profound as it is predictive.
Author: Patrick Mcnutt
Publisher:
ISBN: 9781259071065
Category : Business planning
Languages : en
Pages : 215
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Book Description
Author: Valliappa Lakshmanan
Publisher: O'Reilly Media
ISBN: 1098115759
Category : Computers
Languages : en
Pages : 408
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Book Description
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
Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549
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Book Description
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Author: Andy Clark
Publisher: Oxford University Press, USA
ISBN: 0190217014
Category : Medical
Languages : en
Pages : 425
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Book Description
This title brings together work on embodiment, action, and the predictive mind. At the core is the vision of human minds as prediction machines - devices that constantly try to stay one step ahead of the breaking waves of sensory stimulation, by actively predicting the incoming flow. In every situation we encounter, that complex prediction machinery is already buzzing, proactively trying to anticipate the sensory barrage. The book shows in detail how this strange but potent strategy of self-anticipation ushers perception, understanding, and imagination simultaneously onto the cognitive stage.
Author: Ivet Bahar
Publisher: Garland Science
ISBN: 1351815016
Category : Science
Languages : en
Pages : 336
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Book Description
Protein Actions: Principles and Modeling is aimed at graduates, advanced undergraduates, and any professional who seeks an introduction to the biological, chemical, and physical properties of proteins. Broadly accessible to biophysicists and biochemists, it will be particularly useful to student and professional structural biologists and molecular biophysicists, bioinformaticians and computational biologists, biological chemists (particularly drug designers) and molecular bioengineers. The book begins by introducing the basic principles of protein structure and function. Some readers will be familiar with aspects of this, but the authors build up a more quantitative approach than their competitors. Emphasizing concepts and theory rather than experimental techniques, the book shows how proteins can be analyzed using the disciplines of elementary statistical mechanics, energetics, and kinetics. These chapters illuminate how proteins attain biologically active states and the properties of those states. The book ends with a synopsis the roles of computational biology and bioinformatics in protein science.
Author: Christopher M. Bishop
Publisher: Springer
ISBN: 9781493938438
Category : Computers
Languages : en
Pages : 0
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Book Description
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Author: Nicolo Cesa-Bianchi
Publisher: Cambridge University Press
ISBN: 113945482X
Category : Computers
Languages : en
Pages : 4
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Book Description
This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.
Author: Stefan Jansen
Publisher: Packt Publishing Ltd
ISBN: 1839216786
Category : Business & Economics
Languages : en
Pages : 822
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Book Description
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.
