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I have done this video with 100% practice.

I have done this lecture with 100% practice.

Assignment:The terms you mentioned, such as int32, int64, float32, float64, and float16, are data types commonly used in programming languages to represent different kinds of numerical values. Here's a brief explanation of each data type:Int32 (or int): It stands for a 32-bit signed integer. The "int" data type is used to store whole numbers (positive, negative, or zero) without any fractional parts. The 32-bit refers to the number of bits used to represent the value, which determines the range of numbers that can be stored. In the case of int32, it can store values from approximately -2 billion to +2 billion.Int64 (or long): It stands for a 64-bit signed integer. Similar to int32, it represents whole numbers, but with a larger range. The 64-bit representation allows for much larger integer values to be stored, ranging from approximately -9 quintillion to +9 quintillion.Float32 (or float): It stands for a 32-bit floating-point number. Floating-point numbers are used to represent real numbers (numbers with fractional parts). The 32-bit representation allows for a certain level of precision but with a limited range. Float32 can represent numbers with about 7 decimal digits of precision.Float64 (or double): It stands for a 64-bit floating-point number. Similar to float32, but with a larger range and higher precision. Float64 can represent numbers with about 15 decimal digits of precision.Float16: It stands for a 16-bit floating-point number. It has a smaller range and precision compared to float32 and float64. Float16 is commonly used in applications where memory usage is critical, such as in machine learning models running on specialized hardware.

Assignment: The choice of data type depends on the kind of data you need to store and the precision required for your calculations. If you need to work with integers, choose between int32 and int64 based on the range of values you need to represent. If you need to work with real numbers, choose between float32 and float64 based on the required precision, with float64 providing higher precision at the cost of increased memory usage.

Thanks for guidance and explaining basics of Numpy.

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