Finance capital today [[e-book] ] : corporations and banks in the lasting global slump / / by Francois Chesnais

• Record Nr.: UNINA9910810237203321
• Autore: Chesnais François
• Pubbl/distr/stampa: Leiden, [Netherlands] ; ; Boston, [Massachusetts] : , : Brill, , 2016; ©2016
• ISBN: 90-04-25548-6
• Descrizione fisica: 1 online resource (322 p.)
• Collana: Historical Materialism Book Series, , 1570-1522 ; ; ; Volume 131
• Disciplina: 332/.042
• Soggetti: International finance; Financial institutions, International; Capitalism; Financial crises
• 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 indexes.
• Nota di contenuto: Preliminary Material -- Introduction -- 1 The Historical Setting of the Crisis and Its Original Traits -- 2 Financial Liberalisation and Globalisation from the 1960s onwards and the Return of Financial Crises -- 3 The Notion of Interest-Bearing Capital in the Setting of Present Centralisation and Concentration of Capital -- 4 The Organisational Embodiments of Finance Capital and the Intra-Corporate Division of Surplus Value -- 5 The Internationalisation of Productive Capital and the Formation of Global Oligopolies -- 6 The Operational Modes of tncs in the 2000s -- 7 The Further Globalisation of Financial Assets and Markets and the Expansion of New Forms of Fictitious Capital -- 8 Financialisation and the Transformation of Banking and Credit -- 9 Global Financial Contagion and Systemic Crisis in 2008 -- 10 Global Endemic Financial Instability -- Conclusion -- References -- Glossary of Financial Terms -- Topic Index -- Index of Names.
• Sommario/riassunto: Finance Capital Today is shortlisted for the The Isaac and Tamara Deutscher Memorial Prize 2017. Finance Capital Today presents a rich new analysis of the specific features of contemporary capitalism, notably its truly global nature and its financialisation, calling on Marxist analyses of the concentration, centralisation and globalisation of capital ; and Marx’s theory of interest-bearing and fictitious capital. Chesnais shows how financial globalisation and the exponential growth of financial assets have developed alongside the globalisation of productive capital, paying special attention to the contemporary operations of transnational corporations and global oligopoly. He argues that the macroeconomic perspective is one in which large amounts of capital are looking for profitable investment in a setting of underlying overproduction and low profits. The outcome will be low global growth, repeated financial shocks and the growing interconnection between the environmental and economic crises.

Beginning Anomaly Detection Using Python-Based Deep Learning : With Keras and PyTorch / / by Sridhar Alla, Suman Kalyan Adari

• Record Nr.: UNINA9910349526103321
• Autore: Alla Sridhar
• Pubbl/distr/stampa: Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019
• ISBN: 9781484251775; 1484251776
• Edizione: [1st ed. 2019.]
• Descrizione fisica: 1 online resource (XVI, 416 p. 530 illus.)
• Disciplina: 006.3
• Soggetti: Artificial intelligence; Python (Computer program language); Open source software; Artificial Intelligence; Python; Open Source
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Chapter 1: What is Anomaly Detection? -- Chapter 2: Traditional Methods of Anomaly Detection -- Chapter 3: Introduction to Deep Learning -- Chapter 4: Autoencoders -- Chapter 5: Boltzmann Machines -- Chapter 6: Long Short-Term Memory Models -- Chapter 7: Temporal Convolutional Network -- Chapter 8: Practical Use Cases of ; Anomaly Detection -- Appendix A: Introduction to Keras -- Appendix B: Introduction to PyTorch.
• Sommario/riassunto: Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics of time series-based anomaly detection. By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch. What You'll Learn: Understand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection.