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

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發(fā)表于 2025-3-21 16:08:50 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Introduction to Deep Learning
副標(biāo)題From Logical Calculu
編輯Sandro Skansi
視頻videohttp://file.papertrans.cn/474/473601/473601.mp4
概述Offers a welcome clarity of expression, maintaining mathematical rigor yet presenting the ideas in an intuitive and colourful manner.Includes references to open problems studied in other disciplines,
叢書(shū)名稱(chēng)Undergraduate Topics in Computer Science
圖書(shū)封面Titlebook: Introduction to Deep Learning; From Logical Calculu Sandro Skansi Textbook 2018 Springer International Publishing AG, part of Springer Natu
描述.This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website..Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intellige
出版日期Textbook 2018
關(guān)鍵詞Deep learning; Neural networks; Pattern recognition; Natural language processing; Autoencoders
版次1
doihttps://doi.org/10.1007/978-3-319-73004-2
isbn_softcover978-3-319-73003-5
isbn_ebook978-3-319-73004-2Series ISSN 1863-7310 Series E-ISSN 2197-1781
issn_series 1863-7310
copyrightSpringer International Publishing AG, part of Springer Nature 2018
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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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