Introduction to deep learning : from logical calculus to artificial intelligence / Sandro Skansi.
نوع المادة : نصالسلاسل:Undergraduate topics in computer scienceالناشر:Cham, Switzerland : Springer, 2018وصف:xiii, 191 pages : illustrations ; 24 cmنوع المحتوى:- text
- unmediated
- volume
- 3319730037
- 9783319730035
- Q325.5 .S5 2018
نوع المادة | المكتبة الحالية | رقم الطلب | رقم النسخة | حالة | تاريخ الإستحقاق | الباركود | |
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كتاب | UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة | Q325.5 .S5 2018 (إستعراض الرف(يفتح أدناه)) | C.1 | Library Use Only | داخل المكتبة فقط | 30020000038387 | ||
كتاب | UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة | Q325.5 .S5 2018 (إستعراض الرف(يفتح أدناه)) | C.2 | المتاح | 30020000038386 |
Browsing UAE Federation Library | مكتبة اتحاد الإمارات shelves, Shelving location: General Collection | المجموعات العامة إغلاق مستعرض الرف(يخفي مستعرض الرف)
Q325.5 .S45 2018 The deep learning revolution / | Q325.5 .S45 2018 The deep learning revolution / | Q325.5 .S5 2018 Introduction to deep learning : from logical calculus to artificial intelligence / | Q325.5 .S5 2018 Introduction to deep learning : from logical calculus to artificial intelligence / | Q325.5 .S83 2003 Machine reconstruction of human control strategies / | Q325.5 .S845 2012 Machine learning in non-stationary environments : introduction to covariate shift adaptation / | Q325.5 .S845 2012 Machine learning in non-stationary environments : introduction to covariate shift adaptation / |
Includes bibliographical references and index.
From Logic to Cognitive Science -- Mathematical and Computational Prerequisites -- Machine Learning Basics -- Feedforward Neural Networks -- Modifications and Extensions to a Feed-Forward Neural Network -- Convolutional Neural Networks -- Recurrent Neural Networks -- Autoencoders -- Neural Language Models -- An Overview of Different Neural Network Architectures -- Conclusion.
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 intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.