Implement a single sigmoid neural network unit with weights of [-1.2, -1.1, 3.3, -2.1] Calculate the outputs for two training examples: Example 1: [0.9, 10.0, 3.1, 1] Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Assuming that a sigmoid unit response >0.5 denotes a positive class and <0.5 is negative class, is example 1 positive or negative?  is example 2 positive or negative? Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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The language must be in python.

Just do numbers 5-7

Neural Network Units

  1. Implement a single sigmoid neural network unit with weights of [-1.2, -1.1, 3.3, -2.1]
  2. Calculate the outputs for two training examples:
    Example 1: [0.9, 10.0, 3.1, 1]
    Example 2: [0.9, 2.1, 3.7, 1]
  3. Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold.
  4. Assuming that a sigmoid unit response >0.5 denotes a positive class and <0.5 is negative class, is example 1 positive or negative?  is example 2 positive or negative?
  5. Create a single ReLU unit and provide the outputs for those examples.
  6. Calculate the derivative of the sigmoid with respect to net input for both examples
  7. Calculate the derivative of the ReLU with respect to net input for both examples
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