Titlebook: Machine Learning for Engineers; Using data to solve Ryan G. McClarren Textbook 2021 Springer Nature Switzerland AG 2021 supervised learnin

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Convolutional Neural Networks for Scientific Images and Other Large Data Setsd have a truly huge number of weight and bias parameters to fit during training. For such problems rather than considering each input to be independent, we take advantage of the fact that the input has structure, even if we do not know what that structure is, by using convolutions. In a convolution

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Reinforcement Learning with Policy Gradientse do not know the correct value for the dependent variable, as we would for a supervised learning problem, but we do have an objective function called a reward. We want our machine learning model output to maximize the reward given its inputs. Additionally, the model might need to produce a series o

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