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1. |
Record Nr. |
UNINA9910954614303321 |
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Autore |
Strinati Dominic |
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Titolo |
An introduction to theories of popular culture / / Dominic Strinati |
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Pubbl/distr/stampa |
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London ; ; New York, : Routledge, 2004 |
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ISBN |
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1-134-56507-0 |
0-415-23499-9 |
1-134-56508-9 |
1-280-05694-0 |
0-203-64516-2 |
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Edizione |
[2nd ed.] |
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Descrizione fisica |
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Classificazione |
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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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First published 1995 by Routledge. |
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Nota di bibliografia |
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Includes bibliographical references (p. 247-273) and index. |
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Nota di contenuto |
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1. Mass culture and popular culture -- 2. The Frankfurts School and the culture industry -- 3. Structuralism, semiology and popular culture -- 4. Marxism, political economy and ideology -- 5. Feminism and popular culture -- 6. Postmodernism, contemporary popular culture and recent theoretical developments. |
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Sommario/riassunto |
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Widely recognized as an immensely useful textbook for students of the major theories of popular culture, this is a critical assessment of how these theories have tried to understand and evaluate popular culture in modern societies. |
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2. |
Record Nr. |
UNINA9911007459903321 |
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Autore |
Hijab O |
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Titolo |
Math for Data Science / / by Omar Hijab |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (588 pages) |
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Disciplina |
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Soggetti |
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Mathematics |
Artificial intelligence - Data processing |
Applications of Mathematics |
Data Science |
Dades massives |
Matemàtica |
Aplicacions industrials |
Llibres electrònics |
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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 contenuto |
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Preface -- List of Figures -- Datasets -- Linear Geometry -- Principal Components -- Calculus -- Probability -- Statistics -- Machine Learning -- A. Auxiliary Material -- B. Auxiliary Files -- References -- Python Index -- Index. |
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Sommario/riassunto |
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Math for Data Science presents the mathematical foundations necessary for studying and working in Data Science. The book is suitable for courses in applied mathematics, business analytics, computer science, data science, and engineering. The text covers the portions of linear algebra, calculus, probability, and statistics prerequisite to Data Science. The highlight of the book is the machine learning chapter, where the results of the previous chapters are applied to neural network training and stochastic gradient descent. Also included in this last chapter are advanced topics such as accelerated gradient descent and logistic regression trainability. Clear examples are supported with detailed figures and Python code; Jupyter notebooks and supporting files are available on the author's website. More than 380 exercises and |
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nine detailed appendices covering background elementary material are provided to aid understanding. The book begins at a gentle pace, by focusing on two-dimensional datasets. As the text progresses, foundational topics are expanded upon, leading to deeper results at a more advanced level. |
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