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Deep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks.
Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Reddy Bokka, Karthiek. | |
| 245 | 1 | 0 | |a Deep Learning for Natural Language Processing : |b Solve Your Natural Language Processing Problems with Smart Deep Neural Networks. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2019. | ||
| 300 | |a 1 online resource (372 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Intro; Preface; Introduction to Natural Language Processing; Introduction; The Basics of Natural Language Processing; Importance of natural language processing; Capabilities of Natural language processing; Applications of Natural Language Processing; Text Preprocessing; Text Preprocessing Techniques; Lowercasing/Uppercasing; Exercise 1: Performing Lowercasing on a Sentence; Noise Removal; Exercise 2: Removing Noise from Words; Text Normalization; Stemming; Exercise 3: Performing Stemming on Words; Lemmatization; Exercise 4: Performing Lemmatization on Words; Tokenization | |
| 505 | 8 | |a Exercise 5: Tokenizing WordsExercise 6: Tokenizing Sentences; Additional Techniques; Exercise 7: Removing Stop Words; Word Embeddings; The Generation of Word Embeddings; Word2Vec; Functioning of Word2Vec; Exercise 8: Generating Word Embeddings Using Word2Vec; GloVe; Exercise 9: Generating Word Embeddings Using GloVe; Activity 1: Generating Word Embeddings from a Corpus Using Word2Vec.; Summary; Applications of Natural Language Processing; Introduction; POS Tagging; Parts of Speech; POS Tagger; Applications of Parts of Speech Tagging; Types of POS Taggers; Rule-Based POS Taggers | |
| 505 | 8 | |a Exercise 10: Performing Rule-Based POS TaggingStochastic POS Taggers; Exercise 11: Performing Stochastic POS Tagging; Chunking; Exercise 12: Performing Chunking with NLTK; Exercise 13: Performing Chunking with spaCy; Chinking; Exercise 14: Performing Chinking; Activity 2: Building and Training Your Own POS Tagger; Named Entity Recognition; Named Entities; Named Entity Recognizers; Applications of Named Entity Recognition; Types of Named Entity Recognizers; Rule-Based NERs; Stochastic NERs; Exercise 15: Perform Named Entity Recognition with NLTK | |
| 505 | 8 | |a Exercise 16: Performing Named Entity Recognition with spaCyActivity 3: Performing NER on a Tagged Corpus; Summary; Introduction to Neural Networks; Introduction; Introduction to Deep Learning; Comparing Machine Learning and Deep Learning; Neural Networks; Neural Network Architecture; The Layers; Nodes; The Edges; Biases; Activation Functions; Training a Neural Network; Calculating Weights; The Loss Function; The Gradient Descent Algorithm; Backpropagation; Designing a Neural Network and Its Applications; Supervised neural networks; Unsupervised neural networks | |
| 505 | 8 | |a Exercise 17: Creating a neural networkFundamentals of Deploying a Model as a Service; Activity 4: Sentiment Analysis of Reviews; Summary; Foundations of Convolutional Neural Network; Introduction; Exercise 18: Finding Out How Computers See Images; Understanding the Architecture of a CNN; Feature Extraction; Convolution; The ReLU Activation Function; Exercise 19: Visualizing ReLU; Pooling; Dropout; Classification in Convolutional Neural Network; Exercise 20: Creating a Simple CNN Architecture; Training a CNN; Exercise 21: Training a CNN; Applying CNNs to Text | |
| 500 | |a Exercise 22: Application of a Simple CNN to a Reuters News Topic for Classification | ||
| 520 | |a Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues. | ||
| 542 | |f Copyright © 2019 Packt Publishing |g 2019 | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Natural language processing (Computer science) | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Traitement automatique des langues naturelles. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Natural language processing (Computer science) |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 650 | 7 | |a Deep learning |2 gnd | |
| 650 | 7 | |a Natürliche Sprache |2 gnd | |
| 700 | 1 | |a Hora, Shubhangi. | |
| 700 | 1 | |a Jain, Tanuj. | |
| 700 | 1 | |a Wambugu, Monicah. | |
| 776 | 0 | 8 | |i Print version: |a Reddy Bokka, Karthiek. |t Deep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks. |d Birmingham : Packt Publishing, Limited, ©2019 |z 9781838550295 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781838550295/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a Askews and Holts Library Services |b ASKH |n BDZ0040175075 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL5789190 | ||
| 938 | |a YBP Library Services |b YANK |n 300608063 | ||
| 938 | |a EBSCOhost |b EBSC |n 2159932 | ||
| 994 | |a 92 |b IZTAP | ||