Basic deep learning ( AI ) basics data handling tools in python

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Published On : 2022-01-12
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In this post we will discuss different type of data types and data handling tools in machine learning (DL). we will try to answer what, why , and how to use this tools in deep learning. we will also try to learn perceptional aspects of this tool and how to view this in terms of deep learning. NUMY, PANDAS, TENSOR, LIBEROSA, MATHPLOTLIB.

Technically speaking this are tools in which you put yours data from any source such as excel file, image file, video file, audio file. From this tools you convert all your data to numbers which eventually induce in deep learning architecture. In this tools you can manipulate, clear and generate new data depend on the data present you can do full feature engineering in this tools.

From tools such as (MATHPLOTLIB) you can view and understand your data through visual representation, through graphs as BAR, CHARTS, HISTOGRAM, IMAGE and many other formats. You output data from this tools in numbers in some formats. you convert all your string and categorical data in numbers only. output number will have same shape or dimensions, you can also change shape/dimensions according to your models need. output arrange will have like representation as below

[[1.0 2.o]]

[0.3 0.1]] this is having shape as 2x2 tensor or array






Why we use only this tools ? we use this tools only because they are made keeping consideration of efficiency. This all tools are written in assembly language . NUMPY and PANDAS can literary works with huge files efficiently




Perceptual Thinking


If we consider Neural net as our brain . All information in the brain is passed or reached through blood like wise tools act as a blood in deep learning to pass information to neural nets to process it and gives output





In this post we learned about basic data handling tools in deep learning (AI) . And Perceptual thinking behind using those tools . If you need any help or assistance comment us here . we will be happy to help you .

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