Applied deep learning: a case-based approach to understanding deep neural networks
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Apress
[2018]
|
| Schriftenreihe: | For professionals by professionals
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext http://www.springer.com/ Inhaltsverzeichnis |
| Beschreibung: | Auf der Coverrückseite: Shelve in: Programming languages / Python, user level: Intermediate - advanced |
| Beschreibung: | xxi, 410 Seiten Illustrationen, Diagramme 25.4 cm x 17.8 cm |
| ISBN: | 9781484237892 1484237897 |
Internformat
MARC
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| 245 | 1 | 0 | |a Applied deep learning |b a case-based approach to understanding deep neural networks |c Umberto Michelucci |
| 264 | 1 | |a New York |b Apress |c [2018] | |
| 300 | |a xxi, 410 Seiten |b Illustrationen, Diagramme |c 25.4 cm x 17.8 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a For professionals by professionals | |
| 500 | |a Auf der Coverrückseite: Shelve in: Programming languages / Python, user level: Intermediate - advanced | ||
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| 653 | |a UMA | ||
| 653 | |a Convolutional Neural Networks | ||
| 653 | |a Deep Learning | ||
| 653 | |a Dropout | ||
| 653 | |a Neuron Activation Functions | ||
| 653 | |a Python | ||
| 653 | |a Recursive Neural Networks | ||
| 653 | |a Regularization | ||
| 653 | |a Skilearn | ||
| 653 | |a TensorFlow | ||
| 653 | |a UM | ||
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Datensatz im Suchindex
| _version_ | 1823924576518668288 |
|---|---|
| adam_text |
Table of Contents About the Author. . . xi About the Technical Reviewer. xiii Acknowledgments. . .xv Introduction. . . . xvii Chapter 1: Computational Graphs and TensorFlow. .1 How to Set Up Your Python Environment. .1 Creating an Environment. . .3 Installing TensorFlow. . . .9 Jupyter Notebooks. 11 Basic Introduction to TensorFlow. 14 Computational Graphs. . . 14 Tensors. . . 17 Creating and Running a Computational Graph. 19 Computational Graph with tf.constant. . 19 Computational Graph with tf.Variable. 20 Computational Graph with tf.placeholder. 22 Differences Between run and eval. . 25 Dependencies Between Nodes. . 26 Tips on How to Create and Close a Session. , 27 Chapter 2: Single
Neuron., 31 The Structure of a Neuron. ,31 Matrix Notation. . . ,35 Python Implementation Tip: Loops and NumPy. ,36 Activation Functions. . . ,38
TABLE OF CONTENTS Cost Function and Gradient Descent: The Quirks of the Learning Rate.47 Learning Rate in a Practical Example. . . 50 Example of Linear Regression in tensorflow. . .57 Example of Logistic Regression. Cost Function. 70 70 Activation Function. The Dataset. tensorflow Implementation.75 References. 80 Chapter 3: Feedforward Neural Networks.83 Network Architecture. 84 Output of Neurons. 87 Summary of Matrix Dimensions. 88 Example: Equations for a Network with Three Layers. . 88 Hyperparameters in Fully Connected Networks. 90 softmax Function for Multiclass
Classification. 90 A Brief Digression: Overfitting. 91 A Practical Example of Overfitting. 92 Basic Error Analysis. . 99 The Zalando Dataset. . Building a Model with tensorflow. 105 Network Architecture. Modifying Labels for the softmax Function—One-Hot Encoding.108 The tensorflow Model. Gradient Descent Variations. . Batch Gradient Descent. 114 Stochastic Gradient Descent. . 116 Mini-Batch Gradient Descent.117 Comparison of the Variations.119 Examples of Wrong
Predictions.123 Weight Initialization.125 VI
TABLE OF CONTENTS Adding Many Layers Efficiently. . . . 127 Advantages of Additional Hidden Layers. . 130 Comparing Different Networks. . 131 Tips for Choosing the Right Network. . 135 Chapter 4: Training Neural Networks. Dynamic Learning Rate Decay. .137 .137 ‘139 Iterations or Epochs?. . . Staircase Decay. . . Step Decay. . . . . . .142 Inverse Time Decay. . .145 Exponential Decay. . 148 Natural Exponential Decay. . .150 tensorflow Implementation. . .158 Applying the Methods to the Zalando Dataset . . .162 Common Optimizers. . . . 140 .163 163 Exponentially Weighted Averages. . Momentum. . .167 RMSProp. . .172 Adam. .
.175 Which Optimizer Should I Use?. 177 Example of Self-Developed Optimizer. . 179 Chapter 5: Regularization. .185 Complex Networks and Overfitting. . . .185 What Is Regularization?. . . .190 About Network Complexity. . .191 EpNorm. . . . 192 £2 Regularization. . . 192 Theory of i2 Regularization. . 192 tensorflow Implementation. . Є, Regularization. . . 194 . 205 VII
TABLE OF CONTENTS Theory of Et Regularization and tensorflow Implementation. 206 Are Weights Really Going to Zero?. . 208 Dropout. . . .211 Early Stopping. . . . 215 Additional Methods. . . 216 Chapter 6: Metric Analysis. 217 Human-Level Performance and Bayes Error.218 A Short Story About Human-Level Performance.221 Human-Level Performance on MNIST. . . .223 Bias. Metric Analysis Diagram. Training Set Overfitting. . 225 Test
Set. How to Split Your Dataset. Unbalanced Class Distribution: What Can Happen. Precision, Recall, and F1 Metrics.239 Datasets with Different Distributions.245 К-Fold Cross-Validation. . Manual Metric Analysis: An Example. Chapter 7: Hyperparameter Tuning. 271 Black-Box Optimization. Notes on Black-Box Functions. 273 The Problem of Hyperparameter Tuning.274 Sample Black-Box Problem. Grid Search. 277 Random Search.
. 282 Coarse-to-Fine Optimization.285 Bayesian Optimization. Nadaraya-Watson Regression. Gaussian Process. 291 viii 271
TABLE OF CONTENTS Stationary Process. . 292 Prediction with Gaussian Processes. Acquisition Function. . . 292 298 Upper Confidence Bound (UCB). . .299 Example. 300 Sampling on a Logarithmic Scale.310 Hyperparameter Tuning with the Zalando Dataset. . 312 A Quick Note on the Radial Basis Function. . . 321 Chapter 8: Convolutional and Recurrent Neural Networks. .323 Kernels and Filters. 323 Convolution.325 Examples of Convolution. 334
Pooling. Padding. Building Blocks of a CNN. . . . 346 Convolutional Layers. 347 Pooling Layers. 34 Stacking Layers Together. 34 Example of a CNN. 350 Introduction to RNNs.355 Notation. 357 Basic Idea of RNNs. 358 Why the Name Recurrent?. 359 Learning to Count.359 Chapter 9: A Research Project. 365 The Problem
Description. 365 The Mathematical Model. . 369 Regression Problem. 369 Dataset Preparation. 375 Model Training. ix
TABLE OF CONTENTS Chapter 10: Logistic Regression from Scratch. .391 Mathematics Behind Logistic Regression. . 392 Python Implementation.395 Test of the Model. 398 Dataset Preparation. 398 Running the Test. 400 Conclusion. Index. . x |
| any_adam_object | 1 |
| author | Michelucci, Umberto |
| author_GND | (DE-588)1170709281 |
| author_facet | Michelucci, Umberto |
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| building | Verbundindex |
| bvnumber | BV045244452 |
| classification_rvk | ST 302 ST 300 |
| ctrlnum | (OCoLC)1060778685 (DE-599)DNB1159912017 |
| discipline | Informatik |
| format | Book |
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| spelling | Michelucci, Umberto Verfasser (DE-588)1170709281 aut Applied deep learning a case-based approach to understanding deep neural networks Umberto Michelucci New York Apress [2018] xxi, 410 Seiten Illustrationen, Diagramme 25.4 cm x 17.8 cm txt rdacontent n rdamedia nc rdacarrier For professionals by professionals Auf der Coverrückseite: Shelve in: Programming languages / Python, user level: Intermediate - advanced Deep Learning (DE-588)1135597375 gnd rswk-swf UMA Convolutional Neural Networks Deep Learning Dropout Neuron Activation Functions Python Recursive Neural Networks Regularization Skilearn TensorFlow UM UN Deep Learning (DE-588)1135597375 s DE-604 Apress L.P. (DE-588)1065538766 pbl Erscheint auch als Online-Ausgabe 978-1-4842-3790-8 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=5dd6ec1196b34c58b7e9d5eae79fa300&prov=M&dok_var=1&dok_ext=htm Inhaltstext X:MVB http://www.springer.com/ 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=030632589&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Michelucci, Umberto Applied deep learning a case-based approach to understanding deep neural networks Deep Learning (DE-588)1135597375 gnd |
| subject_GND | (DE-588)1135597375 |
| title | Applied deep learning a case-based approach to understanding deep neural networks |
| title_auth | Applied deep learning a case-based approach to understanding deep neural networks |
| title_exact_search | Applied deep learning a case-based approach to understanding deep neural networks |
| title_full | Applied deep learning a case-based approach to understanding deep neural networks Umberto Michelucci |
| title_fullStr | Applied deep learning a case-based approach to understanding deep neural networks Umberto Michelucci |
| title_full_unstemmed | Applied deep learning a case-based approach to understanding deep neural networks Umberto Michelucci |
| title_short | Applied deep learning |
| title_sort | applied deep learning a case based approach to understanding deep neural networks |
| title_sub | a case-based approach to understanding deep neural networks |
| topic | Deep Learning (DE-588)1135597375 gnd |
| topic_facet | Deep Learning |
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