Their origins go back to Google in 2006, when they were first evaluating whether they should implement either GPUs, FPGAs, or custom ASICs.

This presentation delves into the fascinating and sometimes aggravating world of numerical data types, exploring the evolution, strengths, and weaknesses of decimal, fixed point, floating point, and shared exponent formats over the past 70 years.

The text discusses the challenges and considerations of floating-point arithmetic in computer science. It emphasizes the importance of rounding in floating-point calculations and the implications of different precision levels. Additionally, it highlights the need for careful implementation to ensure correctness and accuracy in programs that rely on floating-point arithmetic.

To round a number to 2 decimals in Python, the usual method is using round(value, significantDigit), but it can behave unexpectedly when the digit before the one being rounded is a 5. To address this, a workaround involves adding a small value to ensure proper rounding. This method allows for traditional rounding commonly used in statistics without needing to import additional libraries like Decimal. By incorporating this workaround into a function, you can achieve the desired rounding results without encountering the issue with numbers ending in 5.