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Mastering Concurrency in Python. Create faster programs using concurrency, asynchronous, multithreading, and parallel programming

Język publikacji: angielskim
Autor:
Quan Nguyen
Mastering Concurrency in Python. Create faster programs using concurrency, asynchronous, multithreading, and parallel programming Quan Nguyen - okladka książki

Mastering Concurrency in Python. Create faster programs using concurrency, asynchronous, multithreading, and parallel programming Quan Nguyen - okladka książki

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Bądź pierwszym, który oceni tę książkę
Stron:
446
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Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. Concurrency and parallelism in Python are essential when it comes to multiprocessing and multithreading; they behave differently, but their common aim is to reduce the execution time. This book serves as a comprehensive introduction to various advanced concepts in concurrent engineering and programming.
Mastering Concurrency in Python starts by introducing the concepts and principles in concurrency, right from Amdahl's Law to multithreading programming, followed by elucidating multiprocessing programming, web scraping, and asynchronous I/O, together with common problems that engineers and programmers face in concurrent programming. Next, the book covers a number of advanced concepts in Python concurrency and how they interact with the Python ecosystem, including the Global Interpreter Lock (GIL). Finally, you'll learn how to solve real-world concurrency problems through examples.
By the end of the book, you will have gained extensive theoretical knowledge of concurrency and the ways in which concurrency is supported by the Python language
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O autorze książki

Quan Nguyen is a Python programmer and machine learning enthusiast. He is interested in solving decision-making problems under uncertainty. Quan has authored several books on Python programming and scientific computing. He is currently pursuing a Ph.D. degree in computer science at Washington University in St. Louis, researching Bayesian methods in machine learning.

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