Python machine learning

Over the last half century, "smartness"―the drive for ubiquitous computing―has become a mandate: a new mode of managing and governing politics, economics, and the environment.Smart phones. Smart cars. Smart homes. Smart cities. The imperative to make our world ever smarter in the face of increasingly complex challenges raises several questions: What is this "smartness mandate?" How has it emerged, and what does it say about our evolving way of understanding―and managing―reality? How have we come to see the planet and its denizens first and foremost as data-collecting instruments? In The Smartness Mandate, Orit Halpern and Robert Mitchell radically suggest that "smartness" is not primarily a technology, but rather an epistemology. Through this lens, they offer a critical exploration of the practices, technologies, and subjects that such an understanding relies upon―above all, artificial intelligence and machine learning. The authors approach these not simply as techniques for solving pr

The entanglements of people and data, code and space, knowledge and power: how data and algorithms shape the world―and shape us within that world.With the emergence of a post-truth world, we have witnessed the dissolution of the common ground on which truth claims were negotiated, individual agency enacted, and public spheres shaped. What happens when, as Nietzsche claimed, there are no facts, but only interpretations? In this book, Mark Shepard examines the entanglements of people and data, code and space, knowledge and power that have produced an uncommon ground―a disaggregated public sphere where the extraction of behavioral data and their subsequent processing and sale have led to the emergence of micropublics of ever-finer granularity. Shepard explores how these new post-truth territories are propagated through machine learning systems and social networks, which shape the public and private spaces of everyday life. He traces the balkanization and proliferation of online news and t

這本書帶領讀者重新思考人工智慧的背後代價。不只是程式與演算法，AI的運作牽涉到龐大的自然資源、人力勞動與個人資料。作者凱特・克勞佛以十多年研究為基礎，揭示AI如何重新分配權力、加劇不平等，並對民主與環境造成深遠影響，是一部兼具深度與警示意義的作品。The hidden costs of artificial intelligence—from natural resources and labor to privacy, equality, and freedom“This study argues that [artificial intelligence] is neither artificial nor particularly intelligent. . . . A fascinating history of the data on which machine-learning systems are trained.”—New Yorker“A valuable corrective to much of the hype surrounding AI and a useful instruction manual for the future.”—John Thornhill, Financial Times“It’s a masterpiece, and I haven’t been able to stop thinking about it.”—Karen Hao, senior editor, MIT Tech ReviewWhat happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? Drawing on more than a decade of research, award‑winning scholar Kate Crawford reveals how AI is a technology of extraction: from the minerals drawn from

New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:Thoroughly updated material throughoutNew chapters on boosting, ad

近年來，人工智能與機器人在全世界範圍內備受關注。但是從2014年開始，國際權威調研機構高德納諮詢公司(Gartner)每年公佈的十大技術中，“大數據”這樣的詞語已經不再出現了。這不是因為“大數據”不再重要，而是意味著自2015年以後，“大數據”已經包含在十大技術裡，或者已經成為這些技術的基礎。“大數據”不僅可以用於構建和應用分析模型，還可以應用於機器學習(Machine Learning)、人工智能等諸多領域。本書從多種觀點出發，闡述了對大數據分析的理解和在環境、大數據企劃與設計層面上需要考慮的問題。

Offering a new perspective, this textbook demystifies the operation of electric machines by providing an integrated understanding of electromagnetic fields, electric circuits, numerical analysis, and computer programming. It presents fundamental concepts in a rigorous manner, emphasising underlying physical modelling assumptions and limitations, and provides detailed explanations of how to implement the finite element method to explore these concepts using Python. It includes explanations of the conversion of concepts into algorithms, and algorithms into code, and examples building in complexity, from simple linear-motion electromagnets to rotating machines. Over 100 theoretical and computational end-of-chapter exercises test understanding, with solutions for instructors and downloadable Python code available online. Ideal for graduates and senior undergraduates studying electric machines, electric machine design and control, and power electronic converters and power systems engineerin

深度學習 最佳入門邁向AI專題實戰 ★★★★★【深度學習】★★★★★ ☆☆☆☆☆【理論】+【實作】☆☆☆☆☆ 這是目前市面上講解【深度學習(Deep Learning)】從基礎到應用最完整的書籍，從基礎數學與統計開始，將演算法的原理解釋得更簡易清晰，協助讀者跨入 AI 的門檻，為避免流於空談，盡量增加應用範例，希望能達到即學即用。多位試閱本書內容的專家與學者全部皆給【五顆星】滿分評價。 整本書採用【最新版TensorFlow】+【大量圖片輔助】，+ 【完整理論解說】+【Python程式實作】以原理與實作，講解下列最熱門的AI主題。 ☆【神經網路(NN)】 ☆【卷積神經網路(CNN)】 ☆【物件偵測(YOLO)】 ☆【光學文字辨識(OCR)】 ☆【車牌辨識(ANPR)】 ☆【人臉辨識】 ☆【生成對抗網路 (GAN)】 ☆【深度偽造 (DeepFake)】 ☆【自然語言處理(NLP)】 ☆【聊天機器人(ChatBot)】 ☆【語音辨識(ASR)】 ☆【強化學習(RL)】 讀者只要遵循本書步驟學習相信必可以徹底認識人工智慧、機器學習與深度學習觀念，邁向浩瀚領域。

Providing a concise and accessible overview of the design, implementation and management of medical software, this textbook will equip students with a solid understanding of critical considerations for both standalone medical software (software as a medical device/SaMD) and software that is integrated into hardware devices. It includes: practical discussion of key regulatory documents and industry standards, and how these translate into concrete considerations for medical software design; detailed coverage of the medical software lifecycle process ; accessible introduction to quality and risk management systems in the context of medical software; succinct coverage of essential topics in data science, machine learning, statistics, cybersecurity, software engineering and healthcare bring readers up-to-speed; six cautionary real-world case studies illustrate the dangers of improper or careless software processes. Accompanied by online resources for instructors, this is the ideal introduct

This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendice

A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learningNew material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays140 new exerc

特色1. 數據處理和數據分析並重。針對金融大數據結構複雜、數據源和格式變換大的特點，充分講述SAS軟件DATA步強大的數據處理功能，強化金融數據處理內容。2. 將SAS數據分析能力強和R和Python靈巧和業界流行的特點相結合，建立融合SAS和R及Python的數據處理和分析教材構架，進行教學大綱、授課教案到教材、案例集和習題集等課程要件建設。3. 以《金融計量經濟學》為知識依托，以海通證券金融工程部、天風證券金融工程部、等有名券商量化金融策略為具體案例內容，以權益（股票）市場和債券（固定收益）市場為主要應用領域，以日數據（daily data）為主、高頻日內數據（intraday data）為輔，建立從數據搜集到數據清理、從投資組合到交易策略和算法交易等金融量化方法的教學案例。創新點1. 填補國內教材空白。作為一款權威的數據處理和統計分析軟件，多年來SAS在國內（尤其是銀行系統）得到了廣泛應用。已出版的教材多以操作和數據處理編程為主，針對金融數據處理和分析的書籍並不多見，尤其講解金融數據分析的SAS過程步（Proc step）的教材難以見到。本教材擬對分析金融時間序列的SAS過程進行詳細和全面的介紹，充分揭示SAS在處理和分析金融時間序列數據上的優勢，填補國內教材空白。2.SAS Viya平臺介紹和R、Python的軟件融合。SAS Viya 2016年推出以來，得到了很快的發展，出現了基於SAS Viya 云分析服務CAS的很多應用，國內一些金融機構也採用了SAS Viya構建自己的數據分析平臺。但國內介紹SAS Viya的書很少，沒有發現介紹利用SAS Viya中的ACTION進行金融數據分析的書籍。對SAS Viya的介紹以及利用SAS Viya中的Econometrics8.4 ACTION、Visal Statistical Analysis ACTION、Deep Learning ACTION等進行金融數據處理和分析的內容，是本教材的創新之處。

The dramatic increase in computer performance has been extraordinary, but not for all computations: it has key limits and structure. Software architects, developers, and even data scientists need to understand how exploit the fundamental structure of computer performance to harness it for future applications. Ideal for upper level undergraduates, Computer Architecture for Scientists covers four key pillars of computer performance and imparts a high-level basis for reasoning with and understanding these concepts: Small is fast – how size scaling drives performance; Implicit parallelism – how a sequential program can be executed faster with parallelism; Dynamic locality – skirting physical limits, by arranging data in a smaller space; Parallelism – increasing performance with teams of workers. These principles and models provide approachable high-level insights and quantitative modelling without distracting low-level detail. Finally, the text covers the GPU and machine-learning accelerat

This book explains the main ideas behind MemComputing, its theoretical foundations and its applicability to a wide variety of combinatorial optimization problems, machine learning, and quantum mechani

What happens to risk as the economic horizon goes to zero and risk is seen as an exposure to a change in state that may occur instantaneously at any time? All activities that have been undertaken statically at a fixed finite horizon can now be reconsidered dynamically at a zero time horizon, with arrival rates at the core of the modeling. This book, aimed at practitioners and researchers in financial risk, delivers the theoretical framework and various applications of the newly established dynamic conic finance theory. The result is a nonlinear non-Gaussian valuation framework for risk management in finance. Risk-free assets disappear and low risk portfolios must pay for their risk reduction with negative expected returns. Hedges may be constructed to enhance value by exploiting risk interactions. Dynamic trading mechanisms are synthesized by machine learning algorithms. Optimal exposures are designed for option positioning simultaneously across all strikes and maturities.

Marketing and sales prioritize AI and machine learning more than any other business department, yet often struggle with how to scale and strategize the opportunities it presents.AI Strategy for Sales and Marketing presents a framework for understanding how AI can boost customer-centricity and sales by creating a connected strategy that delivers value today and into the future. Supported by practical tips and advice throughout, it covers topics including personalization, upskilling, customer experience for both on and offline shopping channels and the importance of using AI responsibly to create consumer trust. Featuring original research and interviews with leading practitioners, it also contains global case studies from organizations in a range of sectors, including Samsung, LinkedIn, PwC, Rolls Royce and Hilton, with insights into the various stages of their adoption journeys. Written by a recognized industry expert, it is an invaluable resource for those wanting to benefit from usin

Marketing and sales prioritize AI and machine learning more than any other business department, yet often struggle with how to scale and strategize the opportunities it presents.AI Strategy for Sales and Marketing presents a framework for understanding how AI can boost customer-centricity and sales by creating a connected strategy that delivers value today and into the future. Supported by practical tips and advice throughout, it covers topics including personalization, upskilling, customer experience for both on and offline shopping channels and the importance of using AI responsibly to create consumer trust. Featuring original research and interviews with leading practitioners, it also contains global case studies from organizations in a range of sectors, including Samsung, LinkedIn, PwC, Rolls Royce and Hilton, with insights into the various stages of their adoption journeys. Written by a recognized industry expert, it is an invaluable resource for those wanting to benefit from usin

Artificial Intelligence-Aided Materials Design: AI-Algorithms and Case Studies on Alloys and Metallurgical Processes describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials, including magnetic alloys, nickel-base superalloys, titanium-base alloys, and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the included MATLAB and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference. Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats Helps readers develop predictive models through AI/ML algorithms by writing their own computer code or usin

For 80 years, mathematics has driven fundamental innovation in computing and communications. This timely book provides a panorama of some recent ideas in mathematics and how they will drive continued innovation in computing, communications and AI in the coming years. It provides a unique insight into how the new techniques that are being developed can be used to provide theoretical foundations for technological progress, just as mathematics was used in earlier times by Turing, von Neumann, Shannon and others. Edited by leading researchers in the field, chapters cover the application of new mathematics in computer architecture, software verification, quantum computing, compressed sensing, networking, Bayesian inference, machine learning, reinforcement learning and many other areas.

With .NET 5's ML.NET and Programming ML.NET, any Microsoft .NET developer can solve serious machine learning problems, increasing their value and competitiveness in some of today's fastest-growing are

「作者在上市 IC 公司上班，每天面對 real world problems。探索如何建立輕量深度學習網路以及發展更有效率的學習方式是他每天的工作。這是一本學理及實務兼具的好工具書。」 － 中央研究院資訊科學研究所所長 廖弘源博士 推薦[搭起 AI 與統計的橋樑]原本統計學與人工智慧是兩個完全不同的領域，然而兩者在近代都有了新的發展進而產生連結。在人工智慧中導入機率與統計的觀念，讓電腦具有自己找出數據之間的關聯性並試圖解決問題的能力，因而出現機器學習 (machine learning) ，再加上電腦計算能力的大幅提升，解決多層類神經網路和大數據之間聯繫性的可能，進而衍生出現今最熱門的深度學習 (deep learning)。不過，大部分電腦相關科系出身的人並不熟悉統計學，因此在更上一層樓的時候容易遇到障礙。有鑒於此，小編在推出《機器學習的數學基礎》(天瓏專業書店年度暢銷第一名) 一書之後，就積極開發 AI 與統計學相關的書籍。在尋尋覓覓之後請到擅長統計與機器學習的黃志勝博士出馬撰寫《機器學習的統計基礎》，首要之務就是讓讀者不要視統計為畏途，因此在書中設計大量範例以降低學習難度，務求讀得懂、做得出來才容易吸收，進而搭好統計與機器學習的橋樑。如果自覺數學已經還給老師了也不用擔心，可以將本書搭配《機器學習的數學基礎》一併完整學習。[學會統計, 由混亂到清晰]本書從讀者在高中就學過的集合與機率論開始，帶您快速複習一遍，並將容易混淆之處多多舉例說明，並將以前似懂非懂的隨機變數、機率分布等觀念再解釋得更清楚。接下來就進入專有名詞特別多的統計學，這也是造成許多人暈頭轉向之處。特別是樣本估計、信賴區間、顯著水準、信心水準、t-分布、假設檢定...等讓人一個頭兩個大的主題，更是務求清楚明瞭。當然本書不可能把完整的統計學全都搬進來，此處只介紹機器學習、深度學習需要用到的統計基礎知識，縮短您的學習時間。然後就進入機器學習的重頭戲，從資料前處理到迴歸、分類模型的建立，以及當數據的特徵數過多時的 PCA、LDA 統計降維法。從類神經網路開始進入深度學習的範疇，包括前向傳遞、梯度下降法與倒傳遞學習法的手算實作，幫助讀者一步步建立深度學習的演算邏輯，並利用參數常規化解決模型過擬合 (over-fitting) 的問題。最後，導入模型評估，例如二元、多元分類模型評估指標、迴歸模型評估指標、4

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; found