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020 _a9781484297452
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024 7 _a10.1007/978-1-4842-9745-2
_2doi
050 4 _aQA76.73.P98
072 7 _aUMX
_2bicssc
072 7 _aCOM051360
_2bisacsh
072 7 _aUMX
_2thema
100 1 _aVostokov, Dmitry.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_968243
245 1 0 _aPython Debugging for AI, Machine Learning, and Cloud Computing
_h[electronic resource] :
_bA Pattern-Oriented Approach /
_cby Dmitry Vostokov.
250 _a1st ed. 2024.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2024.
300 _aXXI, 233 p. 57 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1: Fundamental Vocabulary -- Chapter 2: Pattern-Oriented Debugging -- Chapter 3: Elementary Diagnostics Patterns -- Chapter 4: Debugging Analysis Patterns -- Chapter 5: Debugging Implementation Patterns -- Chapter 6: IDE Debugging in Cloud -- Chapter 7: Debugging Presentation Patterns -- Chapter 8: Debugging Architecture Patterns -- Chapter 9: Debugging Design Patterns -- Chapter 10: Debugging Usage Patterns -- Chapter 11: Case Study: Resource Leaks -- Chapter 12: Case Study: Deadlock -- Chapter 13: Challenges of Python Debugging in Cloud Computing -- Chapter 14: Challenges of Python Debugging in AI and Machine Learning -- Chapter 15: What AI and Machine Learning Can Do for Python Debugging -- Chapter 16: The List of Debugging Patterns.
520 _aThis book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior. The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you’ll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You’ll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performance degradation. This includes tracing, logging, and analyziing memory dumps using native WinDbg and GDB debuggers. Upon completing this book, you will have the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications. You will: Employ a pattern-oriented approach to Python debugging that starts with diagnostics of common software problems Use tips and tricks to get the most out of popular IDEs, notebooks, and command-line Python debugging Understand Python internals for interfacing with operating systems and external modules Perform Python memory dump analysis, tracing, and logging.
650 0 _aPython (Computer program language).
_964608
650 0 _aArtificial intelligence.
_958441
650 0 _aMachine learning.
_955059
650 0 _aCloud Computing.
_958386
650 1 4 _aPython.
_947553
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aMachine Learning.
_955059
650 2 4 _aCloud Computing.
_958386
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9781484297445
776 0 8 _iPrinted edition:
_z9781484297469
776 0 8 _iPrinted edition:
_z9798868807282
856 4 0 _uhttps://doi.org/10.1007/978-1-4842-9745-2
912 _aZDB-2-CWD
912 _aZDB-2-SXPC
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