Python machine learning

Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms。 Specifically， the book distinguishes the inner nature

本書的主要目標是，大家一起來「不寫程式，而學AI」。例如不知Python而亦能用TensorFlow。 當今的AI是屬於機器學習(Machine Learning)的一支，基於神經網路( NN: Neural Network)的深度學習。現在，人人都想去理解AI電腦(即機器)到底是如何學習的，以便掌握AI技術熱潮，捷足先登，踏進這項當今的主流產業(AI人工智慧)。 然而，AI機器學習的演算法(Algorithm)又非常依賴於高等數學的運算式，包括線性、非線性函數、N維矩陣(Array)、張量(Tensor)、微分導數、梯度(Gradient)下降、梯度消失等大家很陌生的數學概念和術語。這些複雜性大大阻礙人人親近AI的機會和途徑。俗語說：「面對複雜，唯有簡單」。所以，尋覓和創造一個簡單、親切的途徑，讓人人都能享受，就能彌平進入AI的高大門檻了。Google推出的AI平台：TensorFlow。雖然它還不能完全實踐的美好境界，但可望它是一個不錯的起點了。於是，本書就將Excel試算表與TensorFlow結合起來，就能實現了。不懂程式設計而能學AI，好比不懂車而能學開車。在本書裡，筆者把與分離開來。於是大多數不諳編程技術者，皆能直接切入，而輕鬆愉快地學習AI了。

你要聽特斯拉馬斯克的或是臉書祖克伯的？前者認為人工智慧（AI）會毁滅人類，後者說不會！其實你應聽Google的，它提供平台讓中小企業也可以搭上人工智慧的特快車！ 將衝擊世界的人工智慧類型基本上是指會自己學習的電腦，也就是所謂的機器學習（Machine Learning）及深度學習（Deep learning），而非過去大家習慣的電腦依程式行事。前者是指機器自己由大量資料中，得出某結論，如給電腦一堆

That Monday afternoon, in high-school gyms across America, kids were battling for the only glory American culture seems to want to dispense to the young these days: sports glory.? But at Dos Pueblos High School in Goleta, California, in a gear-cluttered classroom, a different type of “cool” was brewing.? A physics teacher with a dream – the first public high-school teacher ever to win a MacArthur Genius Award -- had rounded up a band of high-I.Q. students who wanted to put their technical know-how to work.? If you asked these brainiacs what the stakes were that first week of their project, they’d have told you it was all about winning a robotics competition – building the ultimate robot and prevailing in a machine-to-machine contest in front of 25,000 screaming fans at Atlanta’s Georgia Dome.?But for their mentor, Amir Abo-Shaeer, much more hung in the balance. ?The fact was, Amir had in mind a different vision for education, one based not on rote learning -- on absorbing facts and fig

建立智慧系統的概念、工具與技術“這是一本卓越的機器學習資源，有清楚、直觀的解說，以及大量實用技巧。”—François CholletKeras作者，《Deep Learning with Python》作者“這本書詳細介紹以神經網路解決問題的理論與做法；建議想要實際運用ML的人都要看這本書。”—Pete WardenTensorFlow行動主管深度學習經歷了一系列的突破之後，已經大幅推動了整個機

內容介紹: 在 ChatGPT 和 AIGC 應用發光發熱之際，你是不是驚覺：怎麼 AI 已經離我這麼近了！其實 AI 一直都在，只是默默在背後運作，然而多數人還不認識 AI，因而有許多神話或恐懼的想法。-▌如果你是上班族：看到媒體、新聞反覆報導，又說 AI 多厲害，又說 AI 會造成失業，到底 AI 是甚麼？對你的工作有甚麼幫助或影響？有危機意識的你該如何擺脫宿命？▌如果你是新鮮人或轉職者：懂不懂 AI，直接影響未來求職的競爭力。當企業對 AI 求才若渴，你的履歷會需要多點 AI 來加分！不過之前學校教的不多，網路資源又讓人似懂非懂，想要獲得企業青睞，該從何下手？▌如果你是家長：這一代年輕人就是 AI 原生族群，AI 勢必伴隨著他們成長，而 AI 所帶來的影響也是避無可避，想要消弭可能的科技代溝，該怎麼預防？-AI 時代已然揭開序幕，要在這個時代生存，你需要對 AI 有基本素養，首要認識 AI、知道 AI 的能耐與限制，然後懂得運用 AI 來提高效率，彌補自身不足，才不會被改革的浪潮所淹沒。有感於一般大眾對於 AI 的認識有限，就算詳讀許多相關資料，單憑書籍、網路文章的隻字片語，難以對 AI 有深刻體悟。作者將人工智慧看似艱澀難懂的知識，經由淺顯易懂的觀念講解，搭配 40 個零基礎也能玩的實作，化為大家都「看得懂、說得出、做得到」的 AI 素養。沒有程式、沒有數學的學習門檻，帶領你無痛體驗生成式 AI、機器學習、深度學習、自然語言處理、電腦視覺辨識...等各種 AI 技術，不懂程式的你，也可以親手訓練 AI、掌握 AI，讓 AI 成為你的神助手！ 本書特色: ▌專家學者誠摯推薦 ▌王年燦｜致理科技大學 創新設計學院 教授兼院長林仁智｜國立臺北教育大學 數位科技設計系教授兼系主任林筱玫｜台灣人工智慧協會 執行長區國良｜國立清華大學 學習科學與科技研究所 教授張一偉｜凡甲科技總經理張維元｜「資料科學家的工作日常」版主(以上依照姓名筆劃順序排列)\ 一看就懂的 AI 最強入門書 /貼近科技脈動，跟上 AI 技術發展腳步，吸收新知不落伍！● 生成式 AI (Generative AI)● 機器學習 (Machine Learning)● 深度學習 (Deep Learning)● 卷積神經網路 (CNN)● 循環神經網路 (RNN)

The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish betw

The workbook / project manual is designed to help you master key chapter content and apply it in the machine shop. This resource includes review material, plus guided practice operations and projects.

This book provides a comprehensive foundation of machine learning. To answer the questions of what to learn, how to learn, what to get from learning, and how to evaluate, as well as what is meant by learning, the book focuses on the fundamental basics of machine learning, its methodology, theory, algorithms, and evaluations, together with some philosophical thinking on comparison between machine learning and human learning for machinery intelligence.The book is organized as follows: Introduction (Chapter 1), Evaluation (Chapter 2), Supervised learning (Chapters 3, 4, and 5), Unsupervised learning (Chapter 6), Representation learning (Chapter 7), Problem decomposition (Chapter 8), Ensemble learning (Chapter 9), Deep learning (Chapter 10), Application (Chapter 11), and Challenges (Chapter 12).The book can be used as a textbook for college, undergraduate, graduate and PhD students majored in computer science, automation, electronic engineering, communication, ect. It can also be used as

本書作者曾在北美多家智能專業公司任高級架構師，有20年數據挖掘、機器學習方面的設計、開發、管理經驗。他結合自己多年的行業經歷，總結了自己在機器學習方面的知識和實際工程中的經驗，提供了大量一線資料。本書不僅介紹了機器學習中的常用算法，而且給出了具體實施環境和經驗總結。重點介紹了相關算法，包括：相關因子算法、聚類算法、分類算法、回歸與測試算法等。不僅列舉了詳細示例，還介紹了算法在工程實踐中的具體應用，

Machine learning develops intelligent computer systems that are able to generalizefrom previously seen examples. A new domain of machine learning, in which the prediction mustsatisfy the additional co

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure. Machine learning develops intelligent computer systems that are able to genera

This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techn

In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a s

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for th

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern re

This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. Computational finance, an excit

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques.

The goal of structured prediction is to build machine learning models that predictrelational information that itself has structure, such as being composed of multiple interrelatedparts. These models,

A concise, rigorous, yet accessible, account of the fundamentals of constrained optimization theory. Many problems arising in diverse fields such as machine learning, medicine, chemical engineering, s

Learn how today’s businesses can transform themselves by leveraging real-time data and advanced machine learning analytics. This book provides prescriptive guidance for architects and develo

Industrial Strength Empirical Modeling clearly explains the main principles of the different machine learning approaches and offers a methodology of how to integrate these techniques for successful r

The process of inductive inference -- to infer general laws and principles from particular instances -- is the basis of statistical modeling, pattern recognition, and machine learning. The Minimum Des

This book is intended to provide a systematic overview of so-called smart techniques, such as nature-inspired algorithms, machine learning and metaheuristics. Despite their ubiquitous presence and wid

The proceedings of DOSFIAC 2017 consist of papers on topics such as artificial intelligence, machine learning, big data, cloud computing, IOT, smart reservoir, visualization, GIS and SCADA systems. Th

The minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explan

Data mining, or knowledge discovery, has become an indispensable technology for businesses and researchers in many fields. Drawing on work in such areas as statistics, machine learning, pattern recog

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the co

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is u

Boosting is an approach to machine learning based on the idea of creating a highlyaccurate predictor by combining many weak and inaccurate "rules of thumb." A remarkablyrich theory has evolved around

Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the re

Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book vir

A hands-on, code-based guide to leveraging Julia in a variety of scientific and data-driven scenarios Key Features: Augment your basic computing skills with an in-depth introduction to JuliaFocus on topic-based approaches to scientific problems and visualisationBuild on prior knowledge of programming languages such as Python, R, or C/C++Purchase of the print or Kindle book includes a free PDF eBook Book Description: Julia is a well-constructed programming language which was designed for fast execution speed by using just-in-time LLVM compilation techniques, thus eliminating the classic problem of performing analysis in one language and translating it for performance in a second.This book is a primer on Julia's approach to a wide variety of topics such as scientific computing, statistics, machine learning, simulation, graphics, and distributed computing.Starting off with a refresher on installing and running Julia on different platforms, you'll quickly get to grips with the core concept

The award-winning author of The Music of the Primes explores the future of creativity and how machine learning will disrupt, enrich, and transform our understanding of what it means to be human.Can a well-programmed machine do anything a human can—only better? Complex algorithms are buying our groceries, picking our partners, and driving our investments. They can navigate more data than a doctor or lawyer and act with greater precision. For many years we’ve taken solace in the notion that they can’t create. But now that algorithms can learn and adapt, does the future of creativity belong to machines too?It is hard to imagine a better guide to the bewildering world of artificial intelligence than Marcus du Sautoy, a celebrated Oxford mathematician whose work on symmetry in the ninth dimension has taken him to the vertiginous edge of mathematical understanding. In The Creativity Code he considers what machine learning means for the future of creativity. Programs like Deep Dream produce d