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  1. So, one of the nice properties of logistic regression is that the sigmoid function outputs the conditional probabilities of the prediction, the class probabilities.[1]
  2. A sigmoid function is a mathematical function having a characteristic “S”-shaped curve or sigmoid curve.[2]
  3. The sigmoid function is used to program neural networks as an activation function.[2]
  4. Read more on the use of the sigmoid function in the post on programming a simple neural network or review the video on principles of neural nets.[2]
  5. A Sigmoid function is a mathematical function which has a characteristic S-shaped curve.[3]
  6. This is often referred to as the Sigmoid Function in the field of machine learning.[3]
  7. In fact, in the limit of x tending towards infinity, the sigmoid function converges to 1, and towards -1 in the case of negative infinity, but the derivative of the function never reaches zero.[3]
  8. In modern artificial neural networks, it is common to see in place of the sigmoid function, the rectifier, also known as the rectified linear unit, or ReLU, being used as the activation function.[3]
  9. This means that it's very easy to compute the derivative of the sigmoid function if you've already calculated the sigmoid function itself.[4]
  10. To differentiate the sigmoid function we rely on the composite function rule (a.k.a the chain rule) and the fact that \(\frac{d(e^{x})}{dx}=e^{x}\).[4]
  11. The To differentiate the sigmoid function we rely on the composite function rule (a.k.a the chain rule) and the fact that \(\frac{d(e^{x})}{dx}=e^{x}\).[4]
  12. A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve.[5]
  13. Special cases of the sigmoid function include the Gompertz curve (used in modeling systems that saturate at large values of x) and the ogee curve (used in the spillway of some dams).[5]
  14. In general, a sigmoid function is monotonic, and has a first derivative which is bell shaped.[5]
  15. The sigmoid function is shown in more detail in Fig.[6]
  16. Figure 26-7a shows a closer look at the sigmoid function, mathematically described by the equation: FIGURE 26-7.[6]
  17. This is calculated by using the value of the sigmoid function itself.[6]
  18. In machine learning, if we tend to learn a relationship between some features and a binary feature then we use a sigmoid function at the output layer ( which produces the outputs ).[7]
  19. The answers on CrossValidated and Quora all list nice properties of the logistic sigmoid function, but it all seems like we cleverly guessed this function.[8]
  20. In both static and dynamic architectures, we use the hyperbolic tangent sigmoid function as transfer function for hidden neurons and the linear transfer function for output layer.[9]
  21. In this paper we discuss a variant sigmoid function with three parameters that denote the dynamic range, symmetry and slope of the function respectively.[10]
  22. By regulating and modifying the sigmoid function parameter configuration in different layers the error signal problem, oscillation problem and asymmetrical input problem can be reduced.[10]
  23. Sigmoid function (aka logistic function) is moslty picked up as activation function in neural networks.[11]
  24. That’s the derivative of the sigmoid function.[11]
  25. The sigmoid function is often used in neural networks (artificial intelligence) to "squish" values into a range between zero and one.[12]
  26. Special functions can be categorized into three, namely, Ramp function, threshold function, and sigmoid function .[13]
  27. (As Wikipedia and other sources note, the term “sigmoid function” is used to refer to a class of functions with S-shaped curves.[14]
  28. The biggest drawback of the sigmoid function for many analytics practitioners is the so-called “vanishing gradient” problem.[14]
  29. For a 1963 example of how a sigmoid function and curve can be used, see Reference 1.[15]
  30. See how the sigmoid function can also be used in machine learning (ML) in a data center.[15]
  31. When a detailed description is lacking, a sigmoid function is often used.[16]
  32. In general, a sigmoid function is real-valued and differentiable, having either a non-negative or non-positive first derivative which is bell shaped.[16]
  33. I recently rediscovered the sigmoid function.[17]

소스

  1. ↑ Logistic Regression -- Why sigmoid function?
  2. ↑ 2.0 2.1 2.2 Sigmoid function
  3. ↑ 3.0 3.1 3.2 3.3 Sigmoid Function
  4. ↑ 4.0 4.1 4.2 The sigmoid function (a.k.a. the logistic function) and its derivative
  5. ↑ 5.0 5.1 5.2 Sigmoid function
  6. ↑ 6.0 6.1 6.2 Sigmoid Function - an overview
  7. ↑ What are the benefits of using a sigmoid function?
  8. ↑ Why sigmoid function instead of anything else?
  9. ↑ Sigmoid Function - an overview
  10. ↑ 10.0 10.1 The influence of the sigmoid function parameters on the speed of backpropagation learning
  11. ↑ 11.0 11.1 Sigmoid Function as Neural Network Activation Function
  12. ↑ Sigmoid Function
  13. ↑ Sigmoid function
  14. ↑ 14.0 14.1 The Logit and Sigmoid Functions
  15. ↑ 15.0 15.1 How the sigmoid function is used in AI
  16. ↑ 16.0 16.1 Sigmoid function
  17. ↑ Sigmoid function

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  • [{'LOWER': 'sigmoid'}, {'LEMMA': 'function'}]
  • [{'LOWER': 's'}, {'LEMMA': 'curve'}]
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