Titlebook: Introduction to Deep Learning; From Logical Calculu Sandro Skansi Textbook 2018 Springer International Publishing AG, part of Springer Natu

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書目名稱Introduction to Deep Learning影響因子(影響力)




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Infantry

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Feedforward Neural Networks,present these abstract and graphical objects as mathematical objects (vectors, matrices and tensors). Rosenblatt’s perceptron rule is also presented in detail, which makes it clear that a multilayered perceptron is impossible. The Delta rule, as an alternative, is presented, and the idea of iterativ

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Modifications and Extensions to a Feed-Forward Neural Network,em of local minima as one of the main problems in machine learning is explored with all of its intricacies. The main strategy against local minima is the idea of regularization, by adding a regularization parameter when learning. Both L1 and L2 regularizations are explored and explained in detail. T

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Convolutional Neural Networks,regression accepts data, and defines 1D and 2D convolutional layers as a natural extension of the logistic regression. The chapter also details on how to connect the layers and dimensionality problems. The local receptive field is introduced as a core concept of any convolutional architecture and th

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Recurrent Neural Networks, basic settings of learning (sequence to label, sequence to sequence of labels and sequences with no labels) are introduced and explained in probabilistic terms. The role of hidden states is presented in a detailed exposition (with abundant illustrations) in the setting of a simple recurrent network

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Autoencoders,was left out in Chap.?., completing the exposition of the principal component analysis, and demonstrating what a distributed representation is in mathematical terms. The chapter then introduces the main unsupervised learning technique for deep learning, the autoencoder. The structural aspects are pr

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