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Deep learning cookbook : practical recipes to get started quickly / Douwe Osinga

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Publisher number: 1825034Language: English Publisher: Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo : O'Reilly, June 2018Distributor: [Ipswich, Massachusetts] : EBSCO IndustriesEdition: First EditionDescription: 1 Online-Ressource (xv, 234 Seiten)ISBN:
  • 9781491995815
Subject(s): Additional physical formats: 9781491995846 | 1491995815. | 149199584X | 9781491995815. | Erscheint auch als: Deep Learning Cookbook. Druck-Ausgabe First Edition. Beijing : O'Reilly Media Inc., 2018. xv, 234 SeitenRVK: RVK: ST 301Local classification: Lokale Notation: inf 6.21LOC classification:
  • Q325.5
Online resources: Summary: 4.1 Collecting the DataProblem; Solution; Discussion; 4.2 Training Movie Embeddings; Problem; Solution; Discussion; 4.3 Building a Movie Recommender; Problem; Solution; Discussion; 4.4 Predicting Simple Movie Properties; Problem; Solution; Discussion; Chapter 5. Generating Text in the Style of an Example Text; 5.1 Acquiring the Text of Public Domain Books; Problem; Solution; Discussion; 5.2 Generating Shakespeare-Like Texts; Problem; Solution; Discussion; 5.3 Writing Code Using RNNs; Problem; Solution; Discussion; 5.4 Controlling the Temperature of the Output; Problem; Solution; DiscussionSummary: 5.5 Visualizing Recurrent Network ActivationsProblem; Solution; Discussion; Chapter 6. Question Matching; 6.1 Acquiring Data from Stack Exchange; Problem; Solution; Discussion; 6.2 Exploring Data Using Pandas; Problem; Solution; Discussion; 6.3 Using Keras to Featurize Text; Problem; Solution; Discussion; 6.4 Building a Question/Answer Model; Problem; Solution; Discussion; 6.5 Training a Model with Pandas; Problem; Solution; 6.6 Checking Similarities; Problem; Solution; Discussion; Chapter 7. Suggesting Emojis; 7.1 Building a Simple Sentiment Classifier; Problem; Solution; DiscussionSummary: Getting a Balanced Training SetCreating Data Batches; Training, Testing, and Validation Data; Preprocessing of Text; Preprocessing of Images; Conclusion; Chapter 2. Getting Unstuck; 2.1 Determining That You Are Stuck; Problem; Solution; Discussion; 2.2 Solving Runtime Errors; Problem; Solution; Discussion; 2.3 Checking Intermediate Results; Problem; Solution; Discussion; 2.4 Picking the Right Activation Function (for Your Final Layer); Problem; Solution; Discussion; 2.5 Regularization and Dropout; Problem; Solution; Discussion; 2.6 Network Structure, Batch Size, and Learning Rate; ProblemSummary: Intro; Copyright; Table of Contents; Preface; A Brief History of Deep Learning; Why Now?; What Do You Need to Know?; How This Book Is Structured; Conventions Used in This Book; Accompanying Code; O'Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Tools and Techniques; 1.1 Types of Neural Networks; Fully Connected Networks; Convolutional Networks; Recurrent Networks; Adversarial Networks and Autoencoders; Conclusion; 1.2 Acquiring Data; Wikipedia; Wikidata; OpenStreetMap; Twitter; Project Gutenberg; Flickr; The Internet Archive; Crawling; Other Options; 1.3 Preprocessing DataSummary: SolutionDiscussion; Chapter 3. Calculating Text Similarity Using Word Embeddings; 3.1 Using Pretrained Word Embeddings to Find Word Similarity; Problem; Solution; Discussion; 3.2 Word2vec Math; Problem; Solution; Discussion; 3.3 Visualizing Word Embeddings; Problem; Solution; Discussion; 3.4 Finding Entity Classes in Embeddings; Problem; Solution; Discussion; 3.5 Calculating Semantic Distances Inside a Class; Problem; Solution; Discussion; 3.6 Visualizing Country Data on a Map; Problem; Solution; Discussion; Chapter 4. Building a Recommender System Based on Outgoing Wikipedia LinksPPN: PPN: 1026554683Package identifier: Produktsigel: ZDB-4-NLEBK
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