Python machine learning

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-wor

This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpin

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.This book is a general introduction to machine learning that can serve as a textbook f

This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools

This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.

This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different ap

An introduction to machine learning that includes the fundamental techniques, methods, and applications Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concept

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric conve

Machine learning can be a difficult subject if you’re not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the sta

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.Key FeaturesExplore machine learning techniques with prominent ope

Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning,

In Practical Machine Learning with H2O.ai, author Darren Cook introduces readers to H2O, an open-source machine learning package that is gaining popularity in the data science community. This concise

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques in real-world dat

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it foc

Machine learning is an exciting new way to use computers to perform tasks that require the ability to learn from experience. In order to make machine learning a reality, programmers rely on special la

In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is pres

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random for

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for a

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deployin

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning

Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how t

Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible t

As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the mo

The book adopts a tutorial-based approach to introduce the user to Scikit-learn. If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning

The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignm

Explore the multidisciplinary nature of complex networks through machine learning techniquesStatistical and Machine Learning Approaches for Network Analysis provides an accessible framework for struct

SummaryMachine 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 fle

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric conve

"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This

"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This

This book introduces the newly developed Extreme Learning Machine (ELM) including its theories and learning algorithms. ELM is a unified framework of broad type of generalized single-hidden layer feed

中小企業工作流自動化的 AI 落地解決方案n8n 助你 n 臂之力，搞定重複事！n8n 是一套開源的工作流自動化工具，其視覺化的節點編輯介面讓使用者無需深厚的程式背景，即可輕鬆串接各種服務與 API，實現真正意義上的「Low Code」辦公室自動化與 AI 落地。▌使用本機 n8n，強化公司資料安全性隨書附贈 n8n 安裝包與作者開發的 Excel 相關社群節點，讓讀者免用 Docker 即可快速完成 n8n 環境建置，並能連結本地 Ollama 建立 AI Agent，輕鬆做到各種辦公室自動化工作流。▌讓辦公室高重複性工作全面自動化本書主題涵蓋網路資料擷取、AI 文字生成與摘要、Excel 試算表自動化、Email 與 LINE Message 推播，以及本機檔案資料夾處理等實際商業應用場景。▌無痛從本機環境擴展至雲端服務如有雲端應用需求，本書亦說明如何透過 Tunnel 技術打通本機到雲端的連線，並進一步整合 Google 雲端服務的 OAuth，以及 Webhook 與 LINE Bot 的雲端 AI 應用。▌結合 Vibe Coding，打造專屬功能不求人當官方未提供所需的 n8n 節點功能時，只要請 Claude 協助撰寫 Code 節點的對應程式，或本機 Python 程式，就能做出你需要的功能，甚至連 Webhook 的 HTML 表單都難不倒它。除了上述主題，本書更整合 AI Agent 與 Tools，讓 AI 不再只是生成文字的對話引擎，而是能真正「動手做事」的 Agent，讓你用 n8n 打造安全可控、類 OpenClaw 自動化能力的小龍蝦（MiniClaw）。本書特色: ✯ 無腦解壓縮 WinPython 套件即可完成本機 n8n 的安裝，讓所有資料與流程皆在個人電腦上運行。✯ 作者特別針對 Excel 試算表與本機檔案管理的實際需求，開發 5 個 n8n 社群節點：▸ Excel AI：讓 AI 成為試算表的「讀寫與分析」大腦，進行內容分析、資料摘要與自動填寫。▸ Excel API：實現本機 Excel 的「多工與 SQL」操作，支援多人並行處理同一份 Excel 檔案。▸ Excel Watcher：賦予 n8n「監控與即時觸發」能力，打造即時反應的自動化機制。▸ Excel Templater：精準生成格式完整的報表或文件，使其成為

隨著 Google Gemini 3 模型以及各式工具推出，在生成式 AI 的世界中 Google 強勢站穩腳步，再加上 Google 生態系的支援，採用 Google Gemini API 開發 AI Agent 是一個相當不錯的選擇。本書主要解說 Google Gemini API 中關鍵的成員，並利用 Python 版本的 Google GenAI SDK 實作範例，還會帶大家設計當前 AI Agent 的趨勢 -- Skills 機制，主題包含：|簡易 Skills 機制|現今 AI Agent 的共用規範標準就是 Skills，我們會帶大家實作一個簡單的 Skills 機制，讓你的 AI Agent 只要透過 markdown 檔，就可以讓非技術人員的一般使用者也可以客製自動化流程，或是搭配 CLI 工具與 MCP 伺服器等擴充模型能力，而不需要倚賴技術人員修改或是撰寫額外的程式碼。|建構彈性的 MCP 系統|即使有了 Skills 機制，也需要能夠實際完成任務的工具，MCP 就是其中之一。除此之外，MCP 也是擴充模型能力的好幫手，書中會設計讓使用者透過設定檔就能幫 AI Agent 串接各式功能 MCP 伺服器的機制，不論是要讀寫檔案、執行本機指令、操控瀏覽器，或是使用遠端 API 等等，都可以方便達成。|Live API|用講的絕對比用打的更快，書中會用 Live API 實作可隨時中斷插話的即時語音系統，讓你的 AI Agent 彷彿真人用講的就可以操控，而且還會自動記錄交談內容，維持前文脈絡，讓一般使用者透過口說介面就可以使用你的 AI Agent。|把 Google 生態系當靠山|網頁搜尋、找地點誰能比得過 Google，利用 Gemini API 內建工具即可直通 Google 搜尋與地圖，即時資訊一點都不漏，為模型補充新知識。|多模態輸入/輸出|讓你的 AI Agent 像是 NotebookLM 那樣看圖、看影片、聽語音，即使是 YouTube 影片，也可以直接丟連結自動處理。此外，透過 Gemini API，也可以用最強的 Nano Banana Pro 生圖、Veo 3.1 生影片通通沒問題。|詳解 Function Calling 機制|不論是要使用 MCP 伺服器或是 Skill 擴充模型功能、建置自動化流程，最終都需要倚賴 Fu

★全球第一本艾瑞克森取向兒童和青少年心理治療手冊。★艾瑞克森取向療法高度兼容並蓄，可合併敘事治療、家庭治療、依附理論、腦科學，甚至當代心理學的實證發現，綜合運用，制定出家庭端與學校端都能輕易實踐的創意治療方案。★深刻契合台灣學校SEL（社會情緒學習，Social and Emotional Learning ）教育建立正向情緒的關鍵政策。全球第一本艾瑞克森取向兒童和青少年心理治療手冊洞察「問題」中的暗示，順勢而為，即成解方來到診間的孩子，往往帶著各種「問題」。大人試圖將孩子的沉默、衝撞、不合作「扭轉回正途」，卻屢屢挫敗。但從艾瑞克森取向的治療觀點來看，其實無限的改變契機正在眼前攤開！艾瑞克森療法認為，人們所求助的「問題」本身就藏著解決的線索和資源；治療的任務，就是讓它們有機會被喚起。這樣「順勢而為」的治療精神，非常適合應用在兒童和青少年身上。本書教導治療師喚起個案的內在資源，不只幫你快速掌握「量身定做」、「順勢而為」、「策略性」、「去穩定化」、「體驗性」與「自然主義」等艾瑞克森取向六大核心能力，並透過生動的案例故事，演示這些能力是如何一次又一次付諸實踐。八個案例故事涵蓋台灣常見的兒童和青少年心理困境，例如拒學、自閉、憂鬱、社交退縮等，提供的不只是技術，更是看待症狀、關係與改變的獨特視角，能有效幫助治療師、家長、教師與孩子連結，改善互動，化危機為轉機。艾瑞克森取向療法高度兼容並蓄，可合併敘事治療、家庭治療、依附理論、腦科學，甚至當代心理學的實證發現，綜合運用，制定出家庭端與學校端都能輕易實踐的創意治療方案，深刻契合台灣學校SEL（社會情緒學習，Social and Emotional Learning ）教育建立正向情緒的關鍵政策，一起陪伴孩子們渡過成長歷程的風暴。《孩子的「問題」在說話：順勢而為的艾瑞克森取向兒童青少年催眠治療》是首部深入探討如何將強大的艾瑞克森治療模式，應用於兒童與青少年領域的中文著作。書中不僅涵蓋了深刻的個案研究，更為家長與學校系統提供了極具價值的系統性建議。──傑弗瑞•薩德博士米爾頓•艾瑞克森基金會創辦人與主席青少年充滿叛逆的內在和力量，本書透過艾瑞克森式催眠協助轉化叛逆運用力量，為青少年量身訂做了一個非常有效的過程，最終達到改變青少年的內在和行為。本書是一本不可多得的好書，極力推薦。──樊雪春台灣師大教育心理與輔導博士、華人艾瑞克森催眠治療學

★ 第一本 Google Antigravity 繁體中文完整教學 ★ AI 原生開發環境的核心概念與實戰技巧 ★ Agent 架構：理解 AI 代理人如何替你寫程式、做決策 ★ 多 Agent 協作實戰：同時調度多個 AI 開發夥伴 ★ Editor 進階功能全攻略：Tab 補全、Command 指令、Review Changes ★ Rules × Workflows：自訂規則與自動化流程 ★ Skills 技能擴充系統：為 Agent 安裝新能力，從 SkillHub 市集挖寶 ★ Artifacts 產出物完整解說：掌握 Agent 與你非同步溝通的關鍵橋樑 ★ Browser Subagent：讓 AI 操控瀏覽器，自動測試網頁與擷取資料 ★ MCP 工具整合大全：連接資料庫、專案管理、設計工具，無限延伸 Agent 能力 ★ 兩大實戰專案手把手教學：Python 電商後台 + 雲端 Markdown 編輯器 ★ 涵蓋 FAQ 與疑難排解，少走彎路、快速上手 AI 寫程式已經不新鮮，但 Google Antigravity 要做的遠不只是「幫你補全程式碼」。它以 Agent 為核心，建立了一套全新的開發典範：AI 不再是被動回應的聊天機器人，而是能理解你的專案脈絡、遵守你定義的程式開發標準、自主規劃並執行多步驟任務的開發夥伴。它甚至能操控瀏覽器幫你測試網頁、透過 MCP 協議連接外部工具。開發者的角色正從「程式碼的生產者」轉變為「AI 的指揮者」。本書從開發工具的演進脈絡切入，帶你理解這場典範轉移的全貌，以及 Antigravity 在其中扮演的角色。 全書十六章分為四大篇章，構成一條從零到實戰的學習路線。基礎篇帶你熟悉介面操作，完成第一個 Todo List 專案；核心篇深入 Agent Manager 多代理協作、Editor 的 Tab 補全與 Command 指令、Rules 與 Workflows 自訂規則，以及 Skills 技能擴充系統；進階篇拆解 Browser Subagent 瀏覽器自動化、Artifacts 產出物機制，以及 MCP 工具整合生態――從資料庫、專案管理到設計工具，一次掌握。每一章都搭配大量截圖與逐步操作指引，建議你邊讀邊做，這是上手最快的方式。 學工具最怕停在「知道」卻「做不到」。本書特別規劃了兩個完整實戰專案，幫你把