Link: https://en.wikipedia.org/wiki/Gradient
Description: In vector calculus, the gradient is a multi-variable generalization of the derivative. Whereas the ordinary derivative of a function of a single variable is a scalar-valued function, the gradient of a function of several variables is a vector-valued function.Specifically, the gradient of a differentiable function of several variables, at a point , is the vector whose components are the partial ...
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Link: https://arxiv.org/pdf/1611.03530.pdf
Description: U NDERSTANDING DEEP LEARNING REQUIRES RE- THINKING GENERALIZATION Chiyuan Zhang Massachusetts Institute of Technology [email protected] Samy Bengio Google Brain [email protected] Moritz Hardt
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Link: https://arxiv.org/abs/1611.03530
Description: If you have a disability and are having trouble accessing information on this website or need materials in an alternate format, contact [email protected] for [email protected] for assistance.
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Link: https://en.wikipedia.org/wiki/Gradient_theorem
Description: The gradient theorem, also known as the fundamental theorem of calculus for line integrals, says that a line integral through a gradient field can be evaluated by evaluating the original scalar field at the endpoints of the curve.. Let φ : U ⊆ ℝ n → ℝ and γ is any curve from p to q.Then − = ∫ [,] ∇ ⋅.It is a generalization of the fundamental theorem of calculus to any curve ...
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Link: https://betterexplained.com/articles/vector-calculus-understanding-the-gradient/
Description: The gradient is a fancy word for derivative, or the rate of change of a function. It’s a vector (a direction to move) that. Points in the direction of greatest increase of a function (intuition on why)Is zero at a local maximum or local minimum (because there is no single direction of increase)
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Link: http://gradcam.cloudcv.org/
Description: Abstract. We propose a technique for making Convolutional Neural Network (CNN)-based models more transparent by visualizing the regions of input that are "important" for predictions from these models - or visual explanations.
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Link: https://developers.google.com/machine-learning/crash-course/reducing-loss/stochastic-gradient-descent
Description: Estimated Time: 3 minutes In gradient descent, a batch is the total number of examples you use to calculate the gradient in a single iteration. So far, we've assumed that the batch has been the entire data set. When working at Google scale, data sets often contain billions or even hundreds of billions of examples.
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Link: https://developers.google.com/machine-learning/crash-course/reducing-loss/gradient-descent
Description: The math around machine learning is fascinating and we're delighted that you clicked the link to learn more. Please note, however, that TensorFlow handles all the gradient computations for you, so you don't actually have to understand the calculus provided here.
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Link: https://www.sciencedirect.com/science/article/pii/S0020748910002063
Description: Generalization, which is an act of reasoning that involves drawing broad inferences from particular observations, is widely-acknowledged as a quality standard in quantitative research, but is more controversial in qualitative research.
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Link: https://datascienceplus.com/gradient-boosting-in-r/
Description: Boosting is another famous ensemble learning technique in which we are not concerned with reducing the variance of learners like in Bagging where our aim is to reduce the high variance of learners by averaging lots of models fitted on bootstrapped data samples generated with replacement from ...
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