Fundamentals of Corporate Finance (11版)
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Fundamentals of Corporate Finance
作 / 譯 者 : Richard A. Brealey,Stewart C. Myers,Alan J. Marcus
I S B N - 13 : 9781265102593
I S B N -
類 別: 財務管理
版 次: 11 版
年 份: 2023
規 格: 726 頁
出 版 商: McGraw Hill Education
內容簡介
Brealey, Fundamentals of Corporate Finance, 11e, is an introduction to corporate finance focusing on how companies invest in real assets, how they raise the money to pay for the investments, and how those assets ultimately affect the firm's value. It also provides
a broad overview of the financial landscape. The book offers a framework for systematically thinking about most of the important financial problems that both firms and individuals are likely to confront.
目錄
PART I: INTRODUCTION
Ch 1 Goals and Governance of the Corporation
Ch 2 Financial Markets and Institutions
Ch 3 Accounting and Finance
Ch 4 Measuring Corporate Performance
PART II: VALUE
Ch 5 The Time Value of Money
Ch 6 Valuing Bonds
Ch 7 Valuing Stocks
Ch 8 Net Present Value and Other Investment Criteria
Ch 9 Using Discounted Cash-Flow Analysis to Make Investment Decisions
Ch10 Project Analysis
PART III: RISK
Ch11 Introduction to Risk, Return, and the Opportunity Cost of Capital
Ch12 Risk, Return, and Capital Budgeting
Ch13 The Weighted-Average Cost of Capital and Company Valuation
PART IV: FINANCING
Ch14 Introduction to Corporate Financing
Ch15 How Corporations Raise Venture Capital and Issue Securities
PART V: DEBT AND PAYOUT POLICY
Ch16 Debt Policy
Ch17 Payout Policy
PART VI: FINANCIAL ANALYSIS AND PLANNING
Ch18 Long-Term Financial Planning
Ch19 Short-Term Financial Planning
Ch20 Working Capital Management
PART VII: SPECIAL TOPICS
Ch21 Mergers, Acquisitions, and Corporate Control
Ch22 International Financial Management
Ch23 Options
Ch24 Risk Management
PART VIII: CONCLUSION
Ch25 What We Do and Do Not Know about Finance
原價:
1580
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1422
現省:
158元
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Probability and Statistics for Economists 2022 (1版)
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PROBABILITY AND STATISTICS FOR ECONOMISTS 2022 (H)
ISBN: 9780691235943
類別: 經濟學Economics
出版社: PRINCETON UNIVERSITY PRESS
作者: HANSEN
年份: 2022
裝訂別: 精裝
頁數: 416頁
Probability theory is the quantitative language used to handle uncertainty and is the foundation of modern statistics. Probability and Statistics for Economists provides graduate and PhD students with an essential introduction to mathematical probability and statistical theory, which are the basis of the methods used in econometrics. This incisive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of the mathematics that every economist needs to know.
> Covers probability and statistics with mathematical rigor while emphasizing intuitive explanations that are accessible to economics students of all backgrounds
> Discusses random variables, parametric and multivariate distributions, sampling, the law of large numbers, central limit theory, maximum likelihood estimation, numerical optimization, hypothesis testing, and more
> Features hundreds of exercises that enable students to learn by doing
> Includes an in-depth appendix summarizing important mathematical results as well as a wealth of real-world examples
> Can serve as a core textbook for a first-semester PhD course in econometrics and as a companion book to Bruce E. Hansen’s Econometrics
> Also an invaluable reference for researchers and practitioners
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Deep Learning: 用Python進行深度學習的基礎理論實作
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書名:DEEP LEARNING|用PYTHON進行深度學習的基礎理論實作
出版社:歐萊禮
出版年月:201708
條碼:9789864764846
內容簡介
不走捷徑,幫助您真正搞懂「深度學習」的真義
這是一本與「深度學習」有關的書籍。從入門開始說明,一步一步帶領你瞭解深度學習必須具備的知識。本書可以幫助您了解:深度學習究竟是什麼?有何特色?根據何種原理來運作?
從零開始,由實做中學習
本書的目標是,盡量避免使用不瞭解內容的「黑盒子」,以基礎的知識為起點,以容易上手的Python撰寫程式,從動手實作的過程中,一步步深入瞭解深度學習。若以車用書籍來比喻這本書的話,這本書並不屬於汽車駕訓教材,而是希望能夠幫助您瞭解車子的原理,而非教您開車的方法。為了瞭解汽車的結構,必須試著打開車子的引擎蓋,將每個零件都拿起來觀察、操作看看。然後盡量用簡單的形狀,篩選出車子的核心部分,就像組合迷你模型般,製作出這台車子。本書的目標,就是透過製作車子的過程,讓你感受到自己實際可以製作出車子,進而熟悉與車子的相關技術。
本書特色:
.利用最少的外部函式庫,使用Python,從零開始實際執行深度學習的程式。
.說明Python 的用法,讓Python 的初學者也能理解。
.實際執行Python 的原始碼,同時提供讀者手邊可以進行實驗的學習環境。
.從簡單的機器學習問題開始,到最後執行精密辨識影像的系統。
.以淺顯易懂的方式說明深度學習與神經網路理論。
.針對看似複雜的技術,如誤差反向傳播與卷積運算等,利用實際操作方式說明,幫助理解。
.介紹在執行深度學習時,有幫助且實用的技巧,包括決定學習率的方法、權重的預設值等。
.說明Batch Normalization、Dropout、Adam 等最近的趨勢與操作。
.為什麼深度學習很優秀,為什麼加深層數,就能提高辨識準確度,為什麼隱藏層很重要,仔細說明這些「為什麼」。
.介紹自動運作、產生影像、強化學習等深度學習的應用範例。
作者介紹
作者簡介
斎藤康毅
1984年生於長崎縣對馬,畢業於東京工業大學工學院,東京大學研究所學際情報學府學士課程修畢。現在於企業內從事與電腦視覺、機器學習有關的研究開發工作。1984年生於長崎縣對馬,畢業於東京工業大學工學院,東京大學研究所學際情報學府學士課程修畢。現在於企業內從事與電腦視覺、機器學習有關的研究開發工作。
目錄
第一章 Python入門
第二章 感知器
第三章 神經網路
第四章 神經網路的學習
第五章 誤差反向傳播法
第六章 與學習有關的技巧
第七章 卷積神經網路
第八章 深度學習
附錄A Softmax-with-Loss層的計算圖
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Economics and Computation: An Introduction to Algorithmic Game Theory, Computational Social Choice, and Fair Division 2015<SV>
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(特價書4折) Introduction to Automata Theory, Language, and Computation 2/e 2003 (PH) 0-321-21029-8 (2版)
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