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1. |
Record Nr. |
UNINA9910824237703321 |
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Autore |
Durbin Emily |
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Titolo |
Depression 101 / / C. Emily Durbin, PhD |
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Pubbl/distr/stampa |
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New York, NY : , : Springer Publishing Company, LLC, , [2014] |
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©2014 |
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ISBN |
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1-78539-865-2 |
0-8261-7107-9 |
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Edizione |
[[Enhanced Credo edition]] |
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Descrizione fisica |
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1 online resource (338 p.) |
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Collana |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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What is depression? -- How does depression manifest? -- Who is likely to experience depression? -- How does depression affect functioning? -- Why does depression exist? -- What models help us to understand the causes of depression? -- What is the role of personality in depressive disorders? -- How do stress and the environmental context impact depression? -- What genes and biological systems are implicated in depression? -- How can depressive disorders be treated? -- Conclusions: how can we integrate our knowledge of depressive disorders to -- Improve our understanding and treatment of these conditions? |
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Sommario/riassunto |
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Depression, often referred to as the "common cold of psychopathology," is among the most prevalent psychiatric conditions, yet it remains challenging to understand and treat. Depression 101 provides a reader-friendly overview of unipolar and bilpolar depression and provides the most current and intriguing scientific knowledge on this topic. |
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2. |
Record Nr. |
UNINA9910683341803321 |
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Titolo |
AI in the Financial Markets : New Algorithms and Solutions / / edited by Federico Cecconi |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (140 pages) |
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Collana |
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Computational Social Sciences, , 2509-9582 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Natural language processing (Computer science) |
Financial engineering |
Machine learning |
Schools of economics |
Capital market |
Artificial Intelligence |
Natural Language Processing (NLP) |
Financial Technology and Innovation |
Machine Learning |
Agent-based Economics |
Capital Markets |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Chapter 1. Artificial Intelligence and Financial Markets -- Chapter 2. AI, the overall picture -- Chapter 3. Financial markets: values, dynamics, problems -- Chapter 4. The AI's Role in the Great Reset -- Chapter 5. AI Fintech: find out the truth -- Chapter 6. ABM applications to Financial Markets -- Chapter 7. ML application to the Financial Market -- Chapter 8. AI tools for pricing of distressed asset utp and npl loan portfolios -- Chapter 9. More than data science: FuturICT 2.0 -- Chapter 10. Opinion dynamics. |
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Sommario/riassunto |
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This book is divided into two parts, the first of which describes AI as we know it today, in particular the Fintech-related applications. In turn, the |
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second part explores AI models in financial markets: both regarding applications that are already available (e.g. the blockchain supply chain, learning through big data, understanding natural language, or the valuation of complex bonds) and more futuristic solutions (e.g. models based on artificial agents that interact by buying and selling stocks within simulated worlds). The effects of the COVID-19 pandemic are starting to show their financial effects: more companies in a liquidity crisis; more unstable debt positions; and more loans from international institutions for states and large companies. At the same time, we are witnessing a growth of AI technologies in all fields, from the production of goods and services, to the management of socio-economic infrastructures: in medicine, communications, education, and security. The question then becomes: could we imagine integrating AI technologies into the financial markets, in order to improve their performance? And not just limited to using AI to improve performance in high-frequency trading or in the study of trends. Could we imagine AI technologies that make financial markets safer, more stable, and more comprehensible? The book explores these questions, pursuing an approach closely linked to real-world applications. The book is intended for three main categories of readers: (1) management-level employees of companies operating in the financial markets, banks, insurance operators, portfolio managers, brokers, risk assessors, investment managers, and debt managers; (2) policymakers and regulators for financial markets, from government technicians to politicians; and (3) readers curious about technology, both for professional and private purposes, as well as those involved in innovation and research in the private and public spheres. |
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