Deep learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Material type: TextSeries: Adaptive computation and machine learningPublication details: New York : Springer, c2016Description: xxii, 775 p. : ill. (some col. ) ; 24 cmISBN: 9780262035613 (hardcover : alk. paper); 0262035618 (hardcover : alk. paper)Subject(s): Machine learningDDC classification: 006.3/1 LOC classification: Q325.5 | .G66 2016Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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General Circulation | APU Library Open Shelf | Book | Q325.5 .G66 2016 c.1 (Browse shelf (Opens below)) | 1 | Available | 00012808 |
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Q325.5 .C48 2018 c.1 Machine learning and security : protecting systems with data and algorithms / | Q325.5 .G46 2019 c.1 Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : | Q325.5 .G476 2023 c.1 Hands-on machine learning with Scikit-Learn, Keras, and Tensorflow : concepts, tools, and techniques to build intelligent systems / | Q325.5 .G66 2016 c.1 Deep learning / | Q325.5 .M33 1998 c.1 Machine learning and data mining : | Q325.5 .M37 2015 c.1 Machine learning : | Q325.5 .M46 2003 c.1 Advanced lectures on machine learning : |
Includes bibliographical references (pages 711-766) and index.
Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models.
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