Japanese imperialism, 1894-1945 / / W.G. Beasley

• Record Nr.: UNINA9910956236603321
• Autore: Beasley W. G (William G.), <1919-2006.>
• Pubbl/distr/stampa: Oxford [Oxfordshire], : Clarendon Press; New York, : Oxford University Press, 1987
• ISBN: 9786613675828; 9780191501302; 0191501301; 9780191638619; 0191638617; 9781280698866; 1280698861
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (294 p.)
• Collana: Clarendon Paperbacks
• Disciplina: 952.03
• Soggetti: Imperialism; Japan Foreign relations 20th century; Japan History 20th century
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover; Contents; List of Maps; List of Tables; Note on Personal Names, Place-names, Transliteration, and Abbreviations; 1. Introduction: Explanations of Imperialism; 2. The Treaty Port System and Japan; The nature of the treaty port system; The treaty port system in Japan; 3. Modernization and Imperialism; Japanese responses to the West; Wealth and strength; 4. Intervention in Korea, 1894-1895; Japan and Korea before the Sino-Japanese War; Japanese policy in Korea, 1894-1895; 5. The Peace Settlement with China, 1894-1896; Liaotung and Taiwan; The commercial provisions; 6. New Imperialism and the War with Russia, 1895-1905Spheres of influence: Korea and Fukien; The conflict with Russia; 7. Formal and Informal Empire in North-east Asia, 1905-1910; The annexation of Korea; Japan's sphere of influence in Manchuria; 8. Chinese Revolution and World War; The Chinese Revolution and the powers; The Twenty-; one Demands; The origins of co-prosperity; 9. Overseas Trade and Investment, 1895-1930; Foreign trade and colonial trade; Foreign investment; 10. Japan's Territorial Dependencies, 1895-1930; Colonial government and society; Colonial economies; 11. The Treaty Port System in Jeopardy, 1918-1931Japan and the Russian Revolution; Japan and the treaty powers; Japan and Chinese nationalism; 12. The Making of Manchukuo, 1931-1932; Nationalism and militarism; Japan and Manchuria before 1930; The Manchurian Incident; Manchukuo; 13. Japan's New Order in North-east Asia; The advance into China; The New Order; The industrial heartland; 14. Advance to the South; South-east Asia and economic self-sufficiency; 15. The Greater East Asia Co-prosperity Sphere; Political structures; Ideology and economics; 16. Conclusion: The Nature of Japanese ImperialismBibliography; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; U; V; W; X; Y; Z
• Sommario/riassunto: This is a study of the origins and nature of Japanese imperialism from the Sino-Japanese war of 1894-1895 through to 1945. Japan is the only Asian country in modern times to have built both a successful industrial economy and an empire, and it is Professor Beasley's contention that these two phenomena are closely related. Japan's aims were influenced by its experience of western imperialism and its own growing industrialization, but as external circumstances changed and Japan's capacity grew, so did its needs and ambitions. The creation of the Japanese empire is one of the most remarkable expl

Before Machine Learning . Volume 1 Linear Algebra [[electronic resource] ] : a story on fundamental mathematics for A.I. / / Jorge Brasil

• Record Nr.: UNINA9911011856703321
• Autore: Brasil Jorge
• Pubbl/distr/stampa: Birmingham, : Packt Publishing, Limited, 2024
• ISBN: 1-83620-895-2; 1-83620-894-4
• Descrizione fisica: 1 online resource (151 p.)
• Disciplina: 006.3/1
• Soggetti: Machine learning; Artificial intelligence; Algebras, Linear
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di contenuto: Intro -- Contents -- We Start Here -- Why Linear Algebra? -- What Is a Vector? -- Is That a Newborn? Vector Addition -- Wait, Is That Vector on Steroids? Scalar Multiplication -- Where Are You Looking, Vector? The Dot Product -- A Simpler Version of the Universe -- The Vector Space -- But What About a Matrix? -- When a Table Isn't Furniture -- Yoga for Vectors -- Linear Transformations -- Could It Be Love? Matrix and Vector Multiplication -- When It's Not Rude to Ask About Size -- The Determinant -- Let's Go The Other Way Around -- Inverse of a Matrix; Gatherings in Space -- Systems of Linear Equations -- Line Up Vectors! The Eigen Stuff -- I've Got the Power, to Power -- Matrix Diagonalization -- Break Them Down -- Matrix Decomposition -- Me, Myself and I -- The Single Value Decomposition (SVD) -- The Final Stretch -- Principal Component Analysis -- We Did It! -- Blank Page
• Sommario/riassunto: "Unlock the essentials of linear algebra to build a strong foundation for machine learning. Dive into vectors, matrices, and principal component analysis with expert guidance in ""Before Machine Learning Volume 1 - Linear Algebra."" Key Features Comprehensive introduction to linear algebra for machine learning Detailed exploration of vectors and matrices In-depth study of principal component analysis (PCA) Book ; Description In this book, you'll embark on a comprehensive journey through the fundamentals of linear algebra, a critical component for any aspiring machine learning expert. Starting with an introductory overview, the course explains why linear algebra is indispensable for machine learning, setting the stage for deeper exploration. You'll then dive into the concepts of vectors and matrices, understanding their definitions, properties, and practical applications in the field. As you progress, the course takes a closer look at matrix decomposition, breaking down complex matrices into simpler, more manageable forms. This section emphasizes the importance of decomposition techniques in simplifying computations and enhancing data analysis. The final chapter focuses on principal component analysis, a powerful technique for dimensionality reduction that is widely used in machine learning and data science. By the end of the course, you will have a solid grasp of how PCA can be applied to streamline data and improve model performance. This course is designed to provide technical professionals with a thorough understanding of linear algebra's role in machine learning. By the end, you'll be well-equipped with the knowledge and skills needed to apply linear algebra in practical machine learning scenarios. What you will learn Understand the fundamental concepts of vectors and matrices Implement principal component analysis in data reduction Analyze the role of linear algebra in machine learning Enhance problem-solving skills through practical applications Gain the ability to interpret and manipulate high-dimensional data Build confidence in using linear algebra for data science projects Who this book is for This course is ideal for technical professionals, data scientists, aspiring machine learning engineers, and students of computer science or related fields. Additionally, it is beneficial for software developers, engineers, and IT professionals seeking to transition into data science or machine learning roles. A basic understanding of high school-level mathematics is recommended but not required, making it accessible for those looking to build a foundational understanding before diving into more advanced topics.".