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1. |
Record Nr. |
UNINA990008809200403321 |
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Autore |
Razzante, Ruben |
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Titolo |
Manuale di diritto dell'informazione e della comunicazione : con riferimenti alla tutela della privacy e all'editoria on-line / Ruben Razzante |
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Pubbl/distr/stampa |
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ISBN |
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Edizione |
[4. ed.] |
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Descrizione fisica |
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Disciplina |
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Locazione |
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Collocazione |
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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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2. |
Record Nr. |
UNINA9910253968503321 |
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Autore |
Unpingco José |
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Titolo |
Python for probability, statistics, and machine learning / / by José Unpingco |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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Disciplina |
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Soggetti |
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Electrical engineering |
Applied mathematics |
Engineering mathematics |
Statistics |
Mathematical statistics |
Data mining |
Communications Engineering, Networks |
Mathematical and Computational Engineering |
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences |
Probability and Statistics in Computer Science |
Data Mining and Knowledge Discovery |
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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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Getting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation. |
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Sommario/riassunto |
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This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and |
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Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming. Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods; Connects to key open-source Python communities and corresponding modules focused on the latest developments in this area; Outlines probability, statistics, and machine learning concepts using an intuitive visual approach, backed up with corresponding visualization codes. |
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