Simulation and Monte Carlo with Applications in Finance and MCMC 2007 (JW) 978-0-470-85495-2
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<特價399> Simulation and Monte Carlo with Applications in Finance and MCMC 2007 (JW) 978-0-470-85495-2
作者： J.S.DAGPUNAR
版次：1
ISBN：9780470854952
年份：2007
出版社：John Wiley
原價:
1250
售價:
407
現省:
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機器學習實務：資料科學工作流程與應用程式開發及最佳化
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序
前言
關於作者
【PART I基本結構】
chapter 01資料科學家的角色
1.1 介紹
1.2 資料科學家的角色
1.3 結論
chapter 02專案工作流程
2.1 介紹
2.2 資料團隊背景
2.3 敏捷開發與產品專注
2.4 結論
chapter 03誤差量化
3.1 介紹
3.2 量化測量值誤差
3.3 採樣誤差
3.4 誤差傳播
3.5 結論
chapter 04資料編碼與預處理
4.1 介紹
4.2 簡單文字處理
4.3 資訊損失
4.4 結論
chapter 05假設檢定
5.1 介紹
5.2 何謂假設？
5.3 誤差類型
5.4 P 值與信賴區間
5.5 多重測試與 "P-hacking"
5.6 範例
5.7 規劃與背景
5.8 結論
chapter 06資料視覺化
6.1 介紹
6.2 分佈與摘要統計
6.3 時間序列圖
6.4 圖視覺化
6.5 結論
【PART II 演算法與架構】
chapter 07演算法與架構
7.1 介紹
7.2 架構
7.3 模型
7.4 結論
chapter 08比較
8.1 介紹
8.2 Jaccard 距離
8.3 MinHash
8.4 Cosine 相似度
8.5 馬氏距離
8.6 結論
chapter 09迴歸
9.1 介紹
9.2 線性最小平方
9.3 線性迴歸的非線性迴歸
9.4 隨機森林
9.5 結論
chapter 10分類與群集
10.1 介紹
10.2 邏輯迴歸
10.3 貝葉斯推論，單純貝葉斯
10.4 K 平均
10.5 領先特徵向量
10.6 貪婪 Louvain
10.7 最近鄰居
10.8 結論
chapter 11貝葉斯網路
11.1 介紹
11.2 因果圖、條件獨立、Markovity
11.3 D 分離與 Markov 性質
11.4 貝葉斯網路因果圖
11.5 模型適配
11.6 結論
chapter 12降維與潛在變項模型
12.1 介紹
12.2 先驗
12.3 因素分析
12.4 主成分分析
12.5 獨立成分分析
12.6 隱含狄利克雷分布
12.7 結論
chapter 13因果推論
13.1 介紹
13.2 實驗
13.3 觀察：一個例子
13.4 控制阻斷非因果路徑
13.5 機器學習估計量
13.6 結論
chapter 14進階機器學習
14.1 介紹
14.2 最佳化
14.3 神經網路
14.4 結論
【PART III 瓶頸與最佳化】
chapter 15硬體基礎知識
15.1 介紹
15.2 隨機存取記憶體
15.3 非揮發性/固定儲存
15.4 吞吐量
15.5 處理器
15.6 結論
chapter 16軟體基礎知識
16.1 介紹
16.2 換頁
16.3 編索引
16.4 顆粒度
16.5 強固性
16.6 擷取、轉換、載入
16.7 結論
chapter 17軟體架構
17.1 介紹
17.2 主從架構
17.3 N 層/服務導向架構
17.4 微服務
17.5 一大塊
17.6 實際案例（混合架構）
17.7 結論
chapter 18CAP 定理
18.1 介紹
18.2 一致性/同時性
18.3 可用性
18.4 分割容錯
18.5 結論
chapter 19邏輯網路拓撲節點
19.1 介紹
19.2 網路圖
19.3 負載平衡
19.4 快取
19.5 資料庫
19.6 佇列
19.7 結論
參考書
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THERMAL PHYSICS(台灣版) (2版)
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【原文書】
書名：Thermal Physics 2/E
作者：Charles Kittel
ISBN：0716710889
Synopsis
CONGRATULATIONS TO HERBERT KROEMER, 2000 NOBEL LAUREATE FOR PHYSICS
For upper-division courses in thermodynamics or statistical mechanics, Kittel and Kroemer offers a modern approach to thermal physics that is based on the idea that all physical systems can be described in terms of their discrete quantum states, rather than drawing on 19th-century classical mechanics concepts.
"synopsis" may belong to another edition of this title.
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PRINCIPLES OF QUANTUM MECHANICS (2版)
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【原文書】
書名：Principles of Quantum Mechanics 2/e
作者:R. Shankar
出版社：Kluwer Academic Plenum Publishers
ISBN：9780306447907
About this Textbook
Reviews from the First Edition:
"An excellent text … The postulates of quantum mechanics and the mathematical underpinnings are discussed in a clear, succinct manner." (American Scientist)
"No matter how gently one introduces students to the concept of Dirac’s bras and kets, many are turned off. Shankar attacks the problem head-on in the first chapter, and in a very informal style suggests that there is nothing to be frightened of." (Physics Bulletin)
Reviews of the Second Edition:
"This massive text of 700 and odd pages has indeed an excellent get-up, is very verbal and expressive, and has extensively worked out calculational details---all just right for a first course. The style is conversational, more like a corridor talk or lecture notes, though arranged as a text. … It would be particularly useful to beginning students and those in allied areas like quantum chemistry." (Mathematical Reviews)
R. Shankar has introduced major additions and updated key presentations in this second edition of Principles of Quantum Mechanics. New features of this innovative text include an entirely rewritten mathematical introduction, a discussion of Time-reversal invariance, and extensive coverage of a variety of path integrals and their applications. Additional highlights include:
- Clear, accessible treatment of underlying mathematics
- A review of Newtonian, Lagrangian, and Hamiltonian mechanics
- Student understanding of quantum theory is enhanced by separate treatment of mathematical theorems and physical postulates
- Unsurpassed coverage of path integrals and their relevance in contemporary physics
The requisite text for advanced undergraduate- and graduate-level students, Principles of Quantum Mechanics, Second Edition is fully referenced and is supported by many exercises and solutions. The book’s self-contained chapters also make it suitable for independent study as well as for courses in applied disciplines.
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LECTURES IN PARTICLE PHYSICS
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The aim of this book on particle physics is to present the theory in a simple way. The style and organization of the material is unique in that intuition is employed, not formal theory or the Monte Carlo method. This volume attempts to be more physical and less abstract than other texts without degenerating into a presentation of data without interpretation.This book is based on four courses of lectures conducted at Fermilab. It should prove very useful to advanced undergraduates and graduate students.
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THERMODYNAMICS & AN INTRODUCTION TO THERMOSTATISTICS (2版)
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The only text to cover both thermodynamic and statistical mechanics--allowing students to fully master thermodynamics at the macroscopic level. Presents essential ideas on critical phenomena developed over the last decade in simple, qualitative terms. This new edition maintains the simple structure of the first and puts new emphasis on pedagogical considerations. Thermostatistics is incorporated into the text without eclipsing macroscopic thermodynamics, and is integrated into the conceptual framework of physical theory.
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A Guide to Monte Carlo Simulations in Statistical Physics (5版)
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A Guide to Monte Carlo Simulations in Statistical Physics
ISBN13：9781108490146
出版社：Cambridge Univ Pr
作者：David Landau
裝訂／頁數：精裝／578頁
版次：5
出版日：2021/02/28
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A GUIDE TO MONTE CARLO SIMULATIONS IN STATISTICAL PHYSICS 2000 (CAM> 0-521-65366-5
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Artificial Intelligence: A Guide to Intelligent Systems (4版)
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【簡介】
What are the principles behind intelligent systems? How are they built? What are intelligent systems useful for? How do we choose the right tool for the job? These questions are answered by Michael Negnevitsky’s Artificial Intelligence: A Guide to Intelligent Systems.
Unlike many books on computer intelligence, which use complex computer science terminology and are crowded with complex matrix algebra and differential equations, this text demonstrates that the ideas behind intelligent systems are simple and straightforward. This text assumes little or no programming experience as it tackles topics like expert systems, fuzzy systems, artificial neural networks, evolutionary computation, knowledge engineering, and data mining.
【目錄】
Introduction to Intelligent Systems
1.1 Intelligent Machines, or What Machines Can Do
1.2 The History of Artificial Intelligence, or From the ‘Dark Ages’ to Knowledge-based Systems
1.3 Generative AI
1.4 Summary
Questions for Review
References
Expert Systems
2.1 Introduction, or Knowledge Representation Using Rules
2.2 The Main Players in the Expert System Development Team
2.3 Structure of a Rule-based Expert System
2.4 Fundamental characteristics of an expert system
2.5 Forward Chaining and Backward Chaining Inference Techniques
2.6 MEDIA ADVISOR: A Demonstration Rule-based Expert System
2.7 Conflict Resolution
2.8 Uncertainty Management in Rule-based Expert Systems
2.9 Advantages and Disadvantages of Rule-based Expert systems
2.10 Summary
Questions for Review
References
Fuzzy Systems
3.1 Introduction, or What Is Fuzzy Thinking?
3.2 Fuzzy Sets
3.3 Linguistic Variables and Hedges
3.4 Operations of Fuzzy Sets
3.6 Fuzzy Inference
3.7 Building a Fuzzy Expert System
3.8 Summary
Questions for Review
References
Frame-based Systems and Semantic Networks
4.1 Introduction, or What Is a Frame?
4.2 Frames as a Knowledge Representation Technique
4.3 Inheritance in Frame-based Systems
4.4 Methods and Demons
4.5 Interaction of Frames and Rules
4.6 Buy Smart: A Frame-based Expert System
4.7 The Web of Data
4.8 RDF – Resource Description Framework and RDF Triples
4.9 Turtle, RDF Schema and OWL
4.10 Querying the Semantic Web with SPARQL
4.11 Summary
Questions for Review
References
Artificial Neural Networks
5.1 Introduction, or How the Brain Works
5.2 The Neuron as a Simple Computing Element
5.3 The Perceptron
5.4 Multilayer Neural Networks
5.5 Accelerated Learning in Multilayer Neural Networks
5.6 The Hopfield Network
5.7 Bidirectional Associative Memory
5.8 Self-organising Neural Networks
5.9 Reinforcement Learning
5.10 Summary
Questions for Review
References
Deep Learning and Convolutional Neural Networks
6.1 Introduction, or How “Deep” Is a Deep Neural Network?
6.2 Image Recognition or How Machines See the World
6.3 Convolution in Machine Learning
6.4 Activation Functions in Deep Neural Networks
6.5 Convolutional Neural Networks
6.6 Back-propagation Learning in Convolutional Networks
6.7 Batch Normalisation
6.8 Summary
Questions for Review
References
Evolutionary Computation
7.1 Introduction, or Can Evolution Be Intelligent?
7.2 Simulation of Natural Evolution
7.3 Genetic Algorithms
7.4 Why Genetic Algorithms Work
7.5 Maintenance Scheduling with Genetic Algorithms
7.6 Genetic Programming
7.7 Evolution Strategies
7.8 Ant Colony Optimisation
7.9 Particle Swarm Optimisation
7.10 Summary
Questions for Review
References
Hybrid Intelligent Systems
8.1 Introduction, or How to Combine German Mechanics with Italian Love
8.2 Neural Expert Systems
8.3 Neuro-Fuzzy Systems
8.4 ANFIS: Adaptive Neuro-Fuzzy Inference System
8.5 Evolutionary Neural Networks
8.6 Fuzzy Evolutionary Systems
8.7 Summary
Questions for Review
References
Knowledge Engineering
9.1 Introduction, or What Is Knowledge Engineering?
9.2 Will an Expert System Work for My Problem?
9.3 Will a Fuzzy Expert System Work for My Problem?
9.4 Will a Neural Network Work for My Problem?
9.5 Will a Deep Neural Network Work for My Problem?
9.6 Will Genetic Algorithms Work for My Problem?
9.7 Will Particle Swarm Optimisation Work for My Problem?
9.8 Will a Hybrid Intelligent System Work for My Problem?
9.9 Summary
Questions for Review
References
Data Mining and Knowledge Discovery
10.1 Introduction, or What Is Data Mining?
10.2 Statistical Methods and Data Visualisation
10.3 Principal Components Analysis
10.4 Relational Databases and Database Queries
10.5 The Data Warehouse and Multidimensional Data Analysis
10.6 Decision Trees
10.7 Association Rules and Market Basket Analysis
10.8 Summary
Questions for Review
References
Glossary
Index
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現省:
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