Logistic (Sigmoid) function in Statistical and Machine Learning (torch.nn.Sigmoid, tf.math.sigmoid)

In this video, we discuss one of the most important and widely-used functions in statistical, machine, and deep learning, known as logistic or sigmoid function. Specifically, we present two main forms of logistic or sigmoid function using the exponential function. We plot this function using Numpy and Scipy in Python and find the limits of this function. Logistic function or sigmoid function always takes values between 0 and 1, which can be viewed as probability values or scores (e.g., for logistic regression or neural networks). We also find the derivative of the logistic or sigmoid function, which has a closed/analytical form. Therefore, this video is valuable for data scientists who want to better understand the importance of logistic or sigmoid function for analyzing data sets.
#LogisticFunction #SigmoidFunction #LogisticRegression

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