Patterns Predictions And Actions

Author : Moritz Hardt,Benjamin Recht
Publisher : Princeton University Press
Page : 321 pages
File Size : 41,6 Mb
Release : 2022-08-23
Category : Computers
ISBN : 9780691233727

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Patterns, Predictions, and Actions: Foundations of Machine Learning by Moritz Hardt,Benjamin Recht Pdf

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,Benjamin Recht
Publisher : Learningbooks
Page : 0 pages
File Size : 49,9 Mb
Release : 2023-12-15
Category : Computers
ISBN : 9732348089

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PATTERNS, PREDICTIONS, AND ACTIONS by Moritz Hardt,Benjamin Recht Pdf

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 : Peter Harrington
Publisher : Simon and Schuster
Page : 558 pages
File Size : 45,8 Mb
Release : 2012-04-03
Category : Computers
ISBN : 9781638352457

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Machine Learning in Action by Peter Harrington Pdf

Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

Author : Richard S. Sutton,Andrew G. Barto
Publisher : MIT Press
Page : 549 pages
File Size : 47,9 Mb
Release : 2018-11-13
Category : Computers
ISBN : 9780262352703

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Reinforcement Learning, second edition by Richard S. Sutton,Andrew G. Barto Pdf

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 : Patrick Mcnutt
Publisher : Unknown
Page : 215 pages
File Size : 48,9 Mb
Release : 2018-05-25
Category : Business planning
ISBN : 1259071065

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Decoding Strategy by Patrick Mcnutt Pdf

Author : Stefan Jansen
Publisher : Packt Publishing Ltd
Page : 822 pages
File Size : 44,5 Mb
Release : 2020-07-31
Category : Business & Economics
ISBN : 9781839216787

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Machine Learning for Algorithmic Trading by Stefan Jansen Pdf

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.

Author : Andy Clark
Publisher : Oxford University Press, USA
Page : 425 pages
File Size : 42,9 Mb
Release : 2016
Category : Medical
ISBN : 9780190217013

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Surfing Uncertainty by Andy Clark Pdf

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 : Valliappa Lakshmanan,Sara Robinson,Michael Munn
Publisher : O'Reilly Media
Page : 408 pages
File Size : 46,9 Mb
Release : 2020-10-15
Category : Computers
ISBN : 9781098115753

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Machine Learning Design Patterns by Valliappa Lakshmanan,Sara Robinson,Michael Munn Pdf

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 : Andrea Mechelli,Sandra Vieira
Publisher : Academic Press
Page : 412 pages
File Size : 42,9 Mb
Release : 2019-11-14
Category : Medical
ISBN : 9780128157404

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Machine Learning by Andrea Mechelli,Sandra Vieira Pdf

Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. Provides a non-technical introduction to machine learning and applications to brain disorders Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches Covers the main methodological challenges in the application of machine learning to brain disorders Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python

Author : Christopher M. Bishop
Publisher : Springer
Page : 0 pages
File Size : 44,8 Mb
Release : 2016-08-23
Category : Computers
ISBN : 1493938436

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Pattern Recognition and Machine Learning by Christopher M. Bishop Pdf

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 : Paolo Arena,Luca Patanè
Publisher : Springer Science & Business Media
Page : 438 pages
File Size : 46,8 Mb
Release : 2008-12-19
Category : Technology & Engineering
ISBN : 9783540884644

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Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots by Paolo Arena,Luca Patanè Pdf

The basic principles guiding sensing, perception and action in bio systems seem to rely on highly organised spatial-temporal dynamics. In fact, all biological senses, (visual, hearing, tactile, etc.) process signals coming from different parts distributed in space and also show a complex time evolution. As an example, mammalian retina performs a parallel representation of the visual world embodied into layers, each of which r- resents a particular detail of the scene. These results clearly state that visual perception starts at the level of the retina, and is not related uniquely to the higher brain centres. Although vision remains the most useful sense guiding usual actions, the other senses, ?rst of all hearing but also touch, become essential particularly in cluttered conditions, where visual percepts are somehow obscured by environment conditions. Ef?cient use of hearing can be learnt from acoustic perception in animals/insects, like crickets, that use this ancient sense more than all the others, to perform a vital function, like mating.

Author : Nicolo Cesa-Bianchi,Gabor Lugosi
Publisher : Cambridge University Press
Page : 4 pages
File Size : 54,6 Mb
Release : 2006-03-13
Category : Computers
ISBN : 9781139454827

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Prediction, Learning, and Games by Nicolo Cesa-Bianchi,Gabor Lugosi Pdf

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 : Alexander Zai,Brandon Brown
Publisher : Manning Publications
Page : 381 pages
File Size : 48,7 Mb
Release : 2020-04-28
Category : Computers
ISBN : 9781617295430

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Deep Reinforcement Learning in Action by Alexander Zai,Brandon Brown Pdf

Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap

Author : Sanghamitra Bandyopadhyay
Publisher : Springer Science & Business Media
Page : 831 pages
File Size : 51,5 Mb
Release : 2005-12-09
Category : Computers
ISBN : 9783540305064

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Pattern Recognition and Machine Intelligence by Sanghamitra Bandyopadhyay Pdf

This book constitutes the refereed proceedings of the First International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005, held in Kolkata, India in December 2005. The 108 revised papers presented together with 6 keynote talks and 14 invited papers were carefully reviewed and selected from 250 submissions. The papers are organized in topical sections on clustering, feature selection and learning, classification, neural networks and applications, fuzzy logic and applications, optimization and representation, image processing and analysis, video processing and computer vision, image retrieval and data mining, bioinformatics application, Web intelligence and genetic algorithms, as well as rough sets, case-based reasoning and knowledge discovery.

Author : Marc Leman
Publisher : MIT Press
Page : 252 pages
File Size : 40,9 Mb
Release : 2016-09-09
Category : Music
ISBN : 9780262034937

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The Expressive Moment by Marc Leman Pdf

A new way to understand expressive interaction, focusing on the dynamic, fast, pre-reflective processes underlying interactions with music. The expressive moment is that point in time when we grasp a situation and respond quickly, even before we are aware of it. In this book, Marc Leman argues that expression drives this kind of interaction, and he proposes a general framework for understanding expressive interactions. He focuses on the dynamic, fast, and pre-reflective processes underlying our interactions with music—whether we are playing an instrument, dancing, listening, or using new interactive technologies. Music offers a well-established domain for studying these fast and interactive processes, and Leman argues that understanding the power of expressive interaction through music may help us understand cognitive processing in other domains, including language, human action coordination, human-animal interaction, and human-machine interaction. Leman regards expressive interactions with music as energizing and empowering. He argues that music is based on patterns that intervene with a reinforcing loop in the human brain, strengthening learning, motivation, and reward. He argues further that the reinforcing effect is influenced by the interaction flow, by fast processes that handle expressive qualities on the fly. Leman sets out the framework in which expressive interaction is situated, describing, among other things, a pragmatic model of communication in which the fundamental components are enactment and dynamics. He looks in more detail at the cognitive-motivational architecture, discussing sensorimotor and motivational schemes. Finally, he discusses applications for the concepts behind expressive motivation in such fields as sports, entertainment, rehabilitation, multimedia art, and music education.