Python Concurrent Programming: Introduction to Concurrent Programming
Python 3.0+
| Beginner
- 14 Videos | 1h 29m 11s
- Includes Assessment
- Earns a Badge
Explore the general theory of concurrent programming, and examine how to have multiple tasks active at any given point in time. This 14-video course offers an in-depth examination of concurrent programming by using the Python programming language. First, learners will examine the two main forms of concurrent programming, multithreading and multiprocessing, and examine their differences and use cases. Next, you will examine executing multitask sequentially, and with multithreading to save time, and how to use multiprocessing to manage a collection of tasks efficiently. Continue by exploring challenges that programmers encounter when adopting concurrency such as synchronization issues and deadlocks, and how to address these issues. You will examine issues that arise when writing concurrent code, and you will learn how to fix these by using the built-in objects available in Python. Finally, this course examines several of the objects available in the Python language such as queues and pools, which simplify the task of building multithreading and multiprocessing applications.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courserecognize what sequential execution is and what its limitations aredescribe multithreading and compare its performance with a sequential execution of tasksidentify the specific use cases for multithreadingsummarize multiprocessing and contrast it with multithreadingdescribe the implementation of threads and processes in the Python languagerecognize what a race condition is and when it can occur with concurrent programming
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outline how locks can help concurrent tasks synchronize their actions on shared resourcessummarize how semaphores can restrict the number of concurrent tasks accessing a shared resourceidentify the use cases for event and condition objects in Python and distinguish between the tworecognize when a deadlock can occur in an application and the actions you can take to avoid itenumerate the built-in data structures available in Python for concurrent programmingoutline how pools of threads and processes can optimize concurrency in your applicationrecall the different synchronization mechanisms in Python and the conditions necessary for deadlocks
IN THIS COURSE
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1.Course Overview2m 44sUP NEXT
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2.Working with Multiple Tasks5m 18s
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3.An Introduction to Multithreading6m 18s
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4.Applications of Multithreading4m 35s
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5.Multiprocessing8m 13s
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6.Concurrent Programming9m 8s
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7.Challenges with Concurrency6m 59s
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8.Synchronization Using Locks6m 45s
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9.Synchronization Using Semaphores4m 54s
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10.Synchronization Using Events and Conditions8m 3s
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11.Deadlocks8m 28s
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12.Data Structures for Concurrent Tasks6m 30s
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13.Thread and Process Pool6m 23s
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14.Exercise: Introduction to Concurrent Programming4m 53s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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