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190713s2019 enk o 000 0 eng d |
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▼aGBB9B4168▼2bnb |
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▼a019446113▼2Uk |
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▼a1104692494 |
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▼a1838553673 |
020
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▼a9781838553678▼q(electronic bk.) |
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▼a2159932▼b(N▼T) |
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▼a(OCoLC)1107580393▼z(OCoLC)1104692494 |
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▼a9781838553678▼bPackt Publishing |
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▼a9BD5685A-365D-4013-9C82-4F86557B527A▼bOverDrive, Inc.▼nhttp://www.overdrive.com |
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▼aEBLCP▼beng▼epn▼cEBLCP▼dUKMGB▼dOCLCO▼dOCLCF▼dCHVBK▼dOCLCQ▼dYDX▼dUKAHL▼dOCLCO▼dTEFOD▼dOCLCQ▼dN▼d221008 |
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▼aQA76.9.N38▼b.B655 2019 |
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▼a006.35▼223 |
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▼aReddy Bokka, Karthiek. |
245
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00 |
▼aDeep Learning for Natural Language Processing :▼bSolve Your Natural Language Processing Problems with Smart Deep Neural Networks. |
260
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▼aBirmingham :▼bPackt Publishing, Limited,▼c2019. |
300
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▼a1 online resource (372 pages) |
336
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▼atext▼btxt▼2rdacontent |
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▼acomputer▼bc▼2rdamedia |
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▼aonline resource▼bcr▼2rdacarrier |
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▼aExercise 22: Application of a Simple CNN to a Reuters News Topic for Classification |
505
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▼aIntro; 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
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▼aExercise 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
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▼aExercise 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
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▼aExercise 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
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▼aExercise 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 |
520
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▼aStarting 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. |
588
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▼aPrint version record. |
590
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▼aAdded to collection customer.56279.3 |
650
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▼aNatural language processing (Computer science) |
650
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▼aNeural networks (Computer science) |
650
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▼aMachine learning. |
650
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▼aMachine learning.▼2fast▼0(OCoLC)fst01004795 |
650
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▼aNatural language processing (Computer science)▼2fast▼0(OCoLC)fst01034365 |
650
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▼aNeural networks (Computer science)▼2fast▼0(OCoLC)fst01036260 |
650
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▼aDeep learning▼2gnd |
650
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▼aNatu?rliche Sprache▼2gnd |
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▼aElectronic books. |
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▼aHora, Shubhangi. |
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▼aJain, Tanuj. |
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▼aWambugu, Monicah. |
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▼iPrint version:▼aReddy Bokka, Karthiek.▼tDeep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks.▼dBirmingham : Packt Publishing, Limited, 짤2019▼z9781838550295 |
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