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Floating Point Numbers: The Key to Computer Precision Explained
بایگانی مجموعه ها ("فیدهای غیر فعال" status)
When? This feed was archived on January 21, 2025 12:35 (
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What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 444616987 series 3597945
Dive into the fascinating world of floating-point numbers and discover why computers sometimes struggle with simple arithmetic.
In this episode, we explore:
- The IEEE 754 standard: How computers represent decimal numbers using sign, exponent, and mantissa
- Precision challenges: Why floating-point arithmetic can lead to unexpected results in critical systems
- Floating-point quirks: The surprising reason why 0.1 + 0.2 might not equal exactly 0.3 in your code
Tune in for mind-blowing insights into the low-level workings of computer arithmetic and their real-world implications!
Want to dive deeper into this topic? Check out our blog post here: Read more
★ Support this podcast on Patreon ★16 قسمت
بایگانی مجموعه ها ("فیدهای غیر فعال" status)
When?
This feed was archived on January 21, 2025 12:35 (
Why? فیدهای غیر فعال status. سرورهای ما، برای یک دوره پایدار، قادر به بازیابی یک فید پادکست معتبر نبوده اند.
What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 444616987 series 3597945
Dive into the fascinating world of floating-point numbers and discover why computers sometimes struggle with simple arithmetic.
In this episode, we explore:
- The IEEE 754 standard: How computers represent decimal numbers using sign, exponent, and mantissa
- Precision challenges: Why floating-point arithmetic can lead to unexpected results in critical systems
- Floating-point quirks: The surprising reason why 0.1 + 0.2 might not equal exactly 0.3 in your code
Tune in for mind-blowing insights into the low-level workings of computer arithmetic and their real-world implications!
Want to dive deeper into this topic? Check out our blog post here: Read more
★ Support this podcast on Patreon ★16 قسمت
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