Combinatorial Machine Learning

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Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different

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本書以案例驅動的方式講解機器學習算法的知識點，並以Python語言作為基礎開發語言實現算法，包括目前機器學習主流算法的原理、算法流程圖、算法的詳細設計步驟、算法實例、算法應用、算法的改進與優化等環節。全書共分 17 章，前兩章介紹機器學習與 Python 語言的相關基礎知識，後面各章以案例的方式分別介紹線性回歸算法、邏輯回歸算法、K *近鄰算法、PCA 降維算法、k-means算法、支持向量機算法

本書涵蓋Kubernetes架構、部署、核心資源類型、系統擴縮容、存儲卷、網絡插件與網絡本書由淺入深地介紹了一系列常見的Python數據科學問題。書中介紹的實踐項目簡單明瞭，可作為模板快速啟動其他類似項目。通過本書，你將學習如何構建一個Web應用程序以進行數值或分類預測，如何理解文本分析，如何創建強大的交互界面，如何對數據訪問進行安全控制，以及如何利用Web插件實現信用卡付款和捐贈。每章都遵循三個

本書探索了一系列廣泛應用的機器學習技術，用實際例子揭示了常見數據中隱藏的處理技巧和竅門。本書還探討了機器學習的前沿進展，提供了一些關於深度學習和強化學習的範例和指導原則，完美演繹了實操過程及算法原理。本書的目標讀者為那些想瞭解機器學習實踐通過機器學習技術解決現實應用的數據科學家。

本書採用理論與實踐相結合的方式，在簡明扼要地闡明機器學習原理的基礎上，通過大量實例介紹了不同場景下機器學習算法在scikit-learn中的實現及應用。書中還有大量的代碼示例及圖例，便於讀者理解和學習並實際上手操作。另一方面，書中還有很多的延伸閱讀指導，方便讀者系統性地瞭解機器學習領域的現有技術及其發展狀態。

本書講解的是在R語言平臺上使用大數據技術構建可擴展機器學習模型的新技術成果。它全面展示了如何採用機器學習算法在原始數據的基礎上構建機器學習模型。本書還能讓那些希望利用ApacheHadoop、Hive、Pig和Spark來實現可擴展機器學習模型的讀者從中受益。

本書對機器學習的關鍵知識點進行了全面講解，幫助讀者順利完成從理論到實踐的過渡。書中首先介紹用於描述機器學習算法的統計與概率的知識，接著詳細分析機器學習技術的兩類主要方法――生成方法和判別方法，後深入研究了如何使機器學習算法在實際應用中發揮更大的作用。本書提供程序源代碼，便於讀者進行數據分析實踐。本書適合高等院校計算機、統計等專業的研究生和高年級本科生閱讀，同時也適合相關領域的技術人員參考。

本書以新的Spark2.0爲技術基礎，重點講解了如何構建機器學習系統以及如何實現機器學習流程的標準化，這兩點都是目前同類書中沒有的。第1～7章從概念、架構、算法等角度介紹了機器學習的基本概念；第8～12章以實例爲主，詳細講解了機器學習流程標準化涉及的關鍵技術；第13章主要以在綫數據或流式數據爲主介紹了流式計算框架SparkStreaming；第14章重點講解了深度學習的框架TensorFlowOn

這是一本介紹如何應用大數據和機器學習技術來解決投資問題並提高投資績效的專著。近年來，機器學習和數據科學在投資中發揮著越來越大的作用。從本質上講，機器學習模型在挖掘數據背後的非線性關係上要強于傳統模型。借助機器學習和大數據，投資經理能夠做出以往傳統模型無法實現的預測，進而做出明智的決策。然而，在投資中，並不是所有的數據集和機器學習技術都對金融應用有用，也不是所有的機器學習技術都可以“即插即用”。《大數據、機器學習與量化投資》這本書由資深量化分析專家托尼‧吉達主編，彙集了多位業內頗具影響力的專家學者的前沿分享，闡釋如何應用大數據和機器學習技術來解決投資問題並提高投資績效。這本書共有13章，理論嚴謹，案例豐富，內容涵蓋機器學習在投資管理中的應用現狀和前景、另類數據和大數據在宏觀交易中的應用、處理大數據集的難點和解決方案、挖掘社交媒體數據集分析企業文化、使用自然語言處理技術進行投資者情緒分析、基於支持向量回歸的全球戰術性資產配置策略、強化學習和深度學習在投資組合優化中的應用等主題，可以作為量化投資從業者、金融算法研究人員、高等院校計算機專業和金融工程專業的師生以及機器學習愛好者的參考用書。

本書作為數據挖掘的入門讀物，基於真實的數據集進行案例實戰，使用Python數據科學庫，從數據預處理開始一步步介紹數據建模和數據挖掘的過程。主要介紹了數據挖掘的基礎知識、基本工具和實踐方法，通過循序漸進地講解算法，帶領讀者輕鬆踏上數據挖掘之旅。本書採用理論與實踐相結合的方式，呈現了如何使用邏輯回歸進行環境數據檢測，如何使用HMM進行中文分詞，如何利用卷積神經網絡識別雷達剖面圖，如何使用循環神經網絡構建聊天機器人，如何使用樸素貝葉斯算法進行破產預測，如何使用DCGAN網絡進行人臉生成等。本書也涉及神經網絡、在線學習、強化學習、深度學習和大數據處理等內容。本書以人工智能主流編程語言Python 3版本作為數據分析與挖掘實戰的應用工具，從Pyhton的基礎語法開始，陸續介紹了NumPy數值計算、Pandas數據處理、Matplotlib數據可視化、爬蟲和Sklearn數據挖掘等內容。全書共涵蓋16個常用的數據挖掘算法和機器學習實戰項目。通過學習本書內容，讀者可以掌握數據分析與挖掘的理論知識及實戰技能。

內容介紹: 別再傻傻只選一個模型訓練很多模型，卻不知道應該選哪一個？沒有一個模型達標？每個模型都有其優缺點，無法取捨？小朋友才做選擇，大人全都要！你該試試集成式學習！集成式學習是使用 2 種或更多的機器學習演算法，來組合出預測能力更好的模型。DeepMind 已經使用集成式學習來組合多個神經網路，控制 Google 資料中心的運作效能；集成式學習技術也在 Kaggle 平台上，席捲了各個競賽的第一名寶座。因此，集成式學習是建立出更具威力的模型，不可或缺的技術之一。本書會介紹實務上常見的集成式學習演算法，如硬投票、軟投票、堆疊法、自助聚合法、適應提升法、梯度提升法、隨機森林、極端隨機樹等，並且使用熱門的 scikit-learn、Keras、OpenEnsembles、XGBoost 等 Python 函式庫來實作各種不同的集成式學習技術，建構出一個強大的模型。熟稔本書的內容後，不但可以精通集成式學習，在實際情境中面對問題時，亦能具備充分的專業知識判斷適用的集成式學習方法，並成功實作它們。書中採用「做中學」的方式，讓你不僅可以快速掌握理論基礎，也能了解各種集成式學習技術的實作，再加上運用真實世界中的資料集，你將能夠建立出更佳的機器學習模型，以解決各種問題，包含迴歸、分類、分群。現在翻開本書，讓我們一起進入集成式學習的世界，整合你所會的全部技術，打造最強大的模型。 本書特色: ● 繁體中文第 1 本集成式學習專書，告訴你不要再傻傻地只選一個模型● 完整介紹集成式學習中常見的演算法，包含極端隨機樹、堆疊法、自助聚合法、提升法等● 一書掌握實作集成式學習的必備套件，如 Scikit-Learn、OpenEnsembles、XGBoost 等● 用 Python 刻演算法給你看，接著告訴你怎麼用套件。讓你不只會做，還懂為什麼這麼做● 以 5 個實務案例來展示集成式學習的威力● 本書由施威銘研究室監修，內容易讀易懂，並加入大量「小編補充」補充必要知識● 本書 Python 範例程式免費下載

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

Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes deep learning and machine learning accessible to everyday de

Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.

An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming in