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Python map() and lambda() Use Cases and Examples

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 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]

2 Top Real-time AI New Approaches for Learners

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Artificial Intelligence (AI) applications in real-time have two approaches. Those are Applied and Generalized. This post tells you the differences between these two. Artificial Intelligence (AI). Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider smart. How the AI started.  A machine, which has reproducing capabilities, basic arithmetic, and memory are called logical-machines. As enter into a new era, a new thought process created now called AI (Artificial Intelligence) AI Approaches Artificial intelligence has two approaches - Applied and Generalized.  Applied Artificial Intelligence.   The Applied AI is, you can find in systems designed to trade the Stocks or maneuver an autonomous vehicle would fall into this category.  Generalized Artificial Intelligence.   Systems or devices that can, in theory, handle any task – are less common, but this is where some of the most exciting advancements are happening today. Mach