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Libros sobre el tema "Python and libraries of python (SciPy"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 14 de febrero de 2022

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1

Bressert, Eli. SciPy and NumPy: Optimizing & boosting your Python programming. Beijing: O'Reilly, 2012.

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2

Inc, ebrary, ed. Spring Python 1.1: Create powerful and versatile Spring Python applications using pragmatic libraries and useful abstractions. Birmingham, U.K: Packt Open Source, 2010.

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3

Hightower, Richard. Python programming with the Java class libraries: A tutorial for building Web and Enterprise applications. Boston, MA: Addison-Wesley, 2002.

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4

Python programming with the Java class libraries: A tutorial for building Web and Enterprise applications with Jython. Boston, MA: Addison-Wesley, 2003.

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5

Inc, ebrary, ed. Python text processing with NLTK 2.0 cookbook: Over 80 practical recipes for using Python's NLTK suite of libraries to maximize your natural language processing capabilities. Birmingham, U.K: Packt Pub., 2010.

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6

Barber, Nan, ed. Elegant SciPy: The Art of Scientific Python. O'Reilly Media, 2017.

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7

Nanjekye, Joannah. Python 2 and 3 Compatibility: With Six and Python-Future Libraries. Apress, 2017.

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8

Rajagopalan, Gayathri. A Python Data Analyst’s Toolkit: Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics. Apress, 2020.

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9

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib. Apress, 2018.

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10

Pajankar. RASPBERRY PI IMAGE PROCESSING PROGRAMMING: DEVELOP REAL-LIFE EXAMPLES WITH PYTHON, PILLOW, AND SCIPY. Apress, 2019.

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11

Pajankar, Ashwin. Raspberry Pi Image Processing Programming: Develop Real-Life Examples with Python, Pillow, and SciPy. Apress, 2017.

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12

Lewinson, Eryk. Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis. Packt Publishing, 2020.

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13

Ortega, José Manuel. Mastering Python for Networking and Security: Leverage Python scripts and libraries to overcome networking and security issues. Packt Publishing, 2018.

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14

Mining, Ethem. Machine Learning : 4 Books in 1: Basic Concepts + Artificial Intelligence + Python Programming + Python Machine Learning. A Comprehensive Guide to Build Intelligent Systems Using Python Libraries. Everooks Ltd, 2020.

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15

Summerfield, Mark. Python in Practice: Create Better Programs Using Concurrency, Libraries, and Patterns (Developer's Library). Addison-Wesley Professional, 2013.

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16

Chapagain, Anish. Hands-On Web Scraping with Python: Perform advanced scraping operations using various Python libraries and tools such as Selenium, Regex, and others. Packt Publishing, 2019.

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17

Antao, Tiago. Bioinformatics with Python Cookbook: Learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology, 2nd Edition. Packt Publishing, 2018.

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18

Hightower, Richard. Python Programming with the Java(TM) Class Libraries: A Tutorial for Building Web and Enterprise Applications with Jython. Addison-Wesley Professional, 2002.

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19

Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes some introduction to Bayesian approaches to modeling as well as deep learning. Writing small programs to apply machine learning techniques is made easy today by the availability of high-level programming systems. This book offers examples in Python with the machine learning libraries sklearn and Keras. The first four chapters concentrate largely on the practical side of applying machine learning techniques. The book then discusses more fundamental concepts and includes their formulation in a probabilistic context. This is followed by chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.
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