Fast Python Video Edition
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8:52:13
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001. Part 1. Foundational Approaches.mp4
00:40
002. Chapter 1. An urgent need for efficiency in data processing.mp4
11:54
003. Chapter 1. Modern computing architectures and high-performance computing.mp4
11:36
004. Chapter 1. Working with Pythons limitations.mp4
06:48
005. Chapter 1. A summary of the solutions.mp4
04:58
006. Chapter 1. Summary.mp4
01:21
007. Chapter 2. Extracting maximum performance from built-in features.mp4
10:14
008. Chapter 2. Profiling code to detect performance bottlenecks.mp4
08:13
009. Chapter 2. Optimizing basic data structures for speed Lists, sets, and dictionaries.mp4
09:42
010. Chapter 2. Finding excessive memory allocation.mp4
16:33
011. Chapter 2. Using laziness and generators for big-data pipelining.mp4
03:15
012. Chapter 2. Summary.mp4
02:23
013. Chapter 3. Concurrency, parallelism, and asynchronous processing.mp4
21:19
014. Chapter 3. Implementing a basic MapReduce engine.mp4
04:17
015. Chapter 3. Implementing a concurrent version of a MapReduce engine.mp4
09:30
016. Chapter 3. Using multiprocessing to implement MapReduce.mp4
13:41
017. Chapter 3. Tying it all together An asynchronous multithreaded and multiprocessing MapReduce server.mp4
08:50
018. Chapter 3. Summary.mp4
01:49
019. Chapter 4. High-performance NumPy.mp4
25:33
020. Chapter 4. Using array programming.mp4
18:25
021. Chapter 4. Tuning NumPys internal architecture for performance.mp4
11:32
022. Chapter 4. Summary.mp4
02:12
023. Part 2. Hardware.mp4
00:43
024. Chapter 5. Re-implementing critical code with Cython.mp4
05:55
025. Chapter 5. A whirlwind tour of Cython.mp4
15:28
026. Chapter 5. Profiling Cython code.mp4
07:26
027. Chapter 5. Optimizing array access with Cython memoryviews.mp4
05:59
028. Chapter 5. Writing NumPy generalized universal functions in Cython.mp4
03:58
029. Chapter 5. Advanced array access in Cython.mp4
14:54
030. Chapter 5. Parallelism with Cython.mp4
02:03
031. Chapter 5. Summary.mp4
01:56
032. Chapter 6. Memory hierarchy, storage, and networking.mp4
14:14
033. Chapter 6. Efficient data storage with Blosc.mp4
08:46
034. Chapter 6. Accelerating NumPy with NumExpr.mp4
05:46
035. Chapter 6. The performance implications of using the local network.mp4
10:59
036. Chapter 6. Summary.mp4
02:00
037. Part 3. Applications and Libraries for Modern Data Processing.mp4
00:37
038. Chapter 7. High-performance pandas and Apache Arrow.mp4
18:57
039. Chapter 7. Techniques to increase data analysis speed.mp4
08:55
040. Chapter 7. pandas on top of NumPy, Cython, and NumExpr.mp4
08:44
041. Chapter 7. Reading data into pandas with Arrow.mp4
10:57
042. Chapter 7. Using Arrow interop to delegate work to more efficient languages and systems.mp4
10:06
043. Chapter 7. Summary.mp4
01:54
044. Chapter 8. Storing big data.mp4
12:17
045. Chapter 8. Parquet An efficient format to store columnar data.mp4
12:21
046. Chapter 8. 8. Dealing with larger-than-memory datasets the old-fashioned way.mp4
07:11
047. Chapter 8. Zarr for large-array persistence.mp4
18:48
048. Chapter 8. Summary.mp4
01:33
049. Part 4. Advanced Topics.mp4
00:36
050. Chapter 9. Data analysis using GPU computing.mp4
19:31
051. Chapter 9. Using Numba to generate GPU code.mp4
13:07
052. Chapter 9. Performance analysis of GPU code The case of a CuPy application.mp4
14:17
053. Chapter 9. Summary.mp4
01:56
054. Chapter 10. Analyzing big data with Dask.mp4
10:49
055. Chapter 10. The computational cost of Dask operations.mp4
17:19
056. Chapter 10. Using Dasks distributed scheduler.mp4
17:44
057. Chapter 10. Summary.mp4
02:07
058. Appendix A. Setting up the environment.mp4
02:54
059. Appendix A. Installing your own Python distribution.mp4
01:04
060. Appendix A. Using Docker.mp4
00:45
061. Appendix A. Hardware considerations.mp4
02:25
062. Appendix B. Using Numba to generate efficient low-level code.mp4
07:56
063. Appendix B. Writing explicitly parallel functions in Numba.mp4
00:51
064. Appendix B. Writing NumPy-aware code in Numba.mp4
01:40
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Udemy
View courses UdemyStudents take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
- language english
- Training sessions 64
- duration 8:52:13
- Release Date 2024/03/01
