Introduction to deep learning:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Massachusetts ; London, England
The MIT Press
[2018]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xii, 174 Seiten Illustrationen, Diagramme |
| ISBN: | 0262039516 9780262039512 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Contents Preface xi 1 Feed-Forward Neural Nets 1.1 Perceptrons................................................................................... 1.2 Cross-entropy Loss Functions for Neural Nets......................... 1.3 Derivatives and StochasticGradient Descent............................. 1.4 Writing Our Program ................................................................. 1.5 Matrix Representation of Neural Nets..................................... 1.6 Data Independence....................................................................... 1.7 References and Further Readings.............................................. 1.8 Written Exercises.......................................................................... 1 3 9 14 18 21 24 25 26 2 Tensor flow 29 2.1 Tensorflow Preliminaries.............................................................. 29 2.2 A TF Program............................................................................. 33 2.3 Multilayered NNs.......................................................................... 38 2.4 Other Pieces ................................................................................. 42 2.4.1 Checkpointing ............................................................... 42 2.4.2 tensordot ....................................................................... 43 2.4.3 Initialization of TFVariables.......................................... 44 2.4.4 Simplifying TFGraph Creation....................................... 47 2.5 References and Further Readings.............................................. 48 2.6 Written
Exercises.......................................................................... 49 3 Convolutional Neural Networks 3.1 Filters, Strides, and Padding ..................................................... 3.2 A Simple TF Convolution Example........................................... 3.3 Multilevel Convolution................................................................. 3.4 Convolution Details .................................................................... vii 51 52 57 61 64
viii CONTENTS 3.4.1 Biases................................................................................ 3.4.2 Layers with Convolution.................................................. 3.4.3 Pooling............................................................................. References and Further Readings.............................................. Written Exercises.......................................................................... 64 65 66 67 68 4 Word Embeddings and Recurrent NNs 4.1 Word Embeddings for Language Models.................................. 4.2 Building Feed-Forward Language Models.................................. 4.3 Improving Feed-Forward Language Models............................... 4.4 Overfitting .................................................................................... 4.5 Recurrent Networks .................................................................... 4.6 Long Short-Term Memory........................................................... 4.7 References and Further Readings.............................................. 4.8 Written Exercises.......................................................................... 71 71 76 78 79 82 88 92 92 5 Sequence-to-Sequence Learning 95 5.1 The Seq2Seq Paradigm................................................................. 96 5.2 Writing a Seq2Seq MT program.................................................. 99 5.3 Attention in Seq2seq.......................................................................102 5.4 Multilength
Seq2Seq.......................................................................107 5.5 Programming Exercise....................................................................108 5.6 Written Exercises............................................................................. 110 5.7 References and Further Readings................................................. Ill 6 Deep Reinforcement Learning 113 6.1 Value Iteration................................................................................ 114 6.2 Q-learning......................................................................................... 117 6.3 Basic Deep-Q Learning....................................................................119 6.4 Policy Gradient Methods ............................................................. 124 6.5 Actor-Critic Methods ....................................................................130 6.6 Experience Replay..........................................................................133 6.7 References and Further Readings................................................. 134 6.8 Written Exercises....................................................... 134 7 Unsupervised Neural-Network Models 137 7.1 Basic Autoencoding .......................................................................137 7.2 Convolutional Autoencoding.......................................................... 140 7.3 Variational Autoencoding............................................................. 144 7.4 Generative Adversarial Networks................................................. 152 3.5 3.6
CONTENTS 7.5 7.6 ix References and Further Readings................................................. 157 Written Exercises.............................................................................157 A Answers to Selected Exercises 159 A.l Chapter 1........................................................................................ 159 A.2 Chapter 2........................................................................................ 160 A.3 Chapter 3........................................................................................ 160 A.4 Chapter 4........................................................................................ 161 A.5 Chapter 5........................................................................................ 161 A.6 Chapter 6........................................................................................ 162 A.7 Chapter 7........................................................................................ 162 Bibliography 165 Index 169
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| indexdate | 2024-07-10T08:16:46Z |
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| isbn | 0262039516 9780262039512 |
| language | English |
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| publisher | The MIT Press |
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| spelling | Charniak, Eugene 1946- Verfasser (DE-588)1183007523 aut Introduction to deep learning Eugene Charniak Cambridge, Massachusetts ; London, England The MIT Press [2018] xii, 174 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030773779&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Charniak, Eugene 1946- Introduction to deep learning Maschinelles Lernen (DE-588)4193754-5 gnd |
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| title | Introduction to deep learning |
| title_auth | Introduction to deep learning |
| title_exact_search | Introduction to deep learning |
| title_full | Introduction to deep learning Eugene Charniak |
| title_fullStr | Introduction to deep learning Eugene Charniak |
| title_full_unstemmed | Introduction to deep learning Eugene Charniak |
| title_short | Introduction to deep learning |
| title_sort | introduction to deep learning |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen |
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