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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

The Growth of Machine Learning till TensorFlow

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The Internet and the vast amount of data are inspirations for CEOs of big corporations to start to use Machine learning. It is to provide a better experience to users. How TensorFlow Starts Let us take Amazon, online retail that uses Machine learning. The algorithm's purpose is to generate revenue. Based on user search data, the ML application provides information or insights. The other example is the advertising platform where Google is a leader in this line. Where it shows ads based on the user movements while surfing the web. These are just a few, but there are many in reality. Machine Learning Evolution Top ML Frameworks Torch The torch is the first framework developed in 2002 by Ronan Collobert. Initially, IBM and Facebook have shown much interest. The interface language is Lua. The primary focus is matrix calculations. It is suitable for developing neural networks. Theano It is developed in 2010 by the University of Montreal. It is highly reliable to process graphs (GPU). The