| 000 | camMi | |
| 001 | 2210080896963 | |
| 003 | OCoLC | |
| 005 | 20210225114920 | |
| 006 | m d | |
| 007 | cr |n|---||||| | |
| 008 | 190713s2019 enk o 000 0 eng d | |
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| 019 | ▼a1104692494 | |
| 020 | ▼a1838553673 | |
| 020 | ▼a9781838553678▼q(electronic bk.) | |
| 035 | ▼a2159932▼b(N▼T) | |
| 035 | ▼a(OCoLC)1107580393▼z(OCoLC)1104692494 | |
| 037 | ▼a9781838553678▼bPackt Publishing | |
| 037 | ▼a9BD5685A-365D-4013-9C82-4F86557B527A▼bOverDrive, Inc.▼nhttp://www.overdrive.com | |
| 040 | ▼aEBLCP▼beng▼epn▼cEBLCP▼dUKMGB▼dOCLCO▼dOCLCF▼dCHVBK▼dOCLCQ▼dYDX▼dUKAHL▼dOCLCO▼dTEFOD▼dOCLCQ▼dN▼d221008 | |
| 050 | ▼aQA76.9.N38▼b.B655 2019 | |
| 082 | ▼a006.35▼223 | |
| 100 | ▼aReddy Bokka, Karthiek. | |
| 245 | 00 | ▼aDeep Learning for Natural Language Processing :▼bSolve Your Natural Language Processing Problems with Smart Deep Neural Networks. |
| 260 | ▼aBirmingham :▼bPackt Publishing, Limited,▼c2019. | |
| 300 | ▼a1 online resource (372 pages) | |
| 336 | ▼atext▼btxt▼2rdacontent | |
| 337 | ▼acomputer▼bc▼2rdamedia | |
| 338 | ▼aonline resource▼bcr▼2rdacarrier | |
| 500 | 00 | ▼aExercise 22: Application of a Simple CNN to a Reuters News Topic for Classification |
| 505 | ▼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 | ▼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 | ▼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 | ▼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 | ▼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 | ▼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 | ▼aPrint version record. | |
| 590 | ▼aAdded to collection customer.56279.3 | |
| 650 | ▼aNatural language processing (Computer science) | |
| 650 | ▼aNeural networks (Computer science) | |
| 650 | ▼aMachine learning. | |
| 650 | ▼aMachine learning.▼2fast▼0(OCoLC)fst01004795 | |
| 650 | ▼aNatural language processing (Computer science)▼2fast▼0(OCoLC)fst01034365 | |
| 650 | ▼aNeural networks (Computer science)▼2fast▼0(OCoLC)fst01036260 | |
| 650 | ▼aDeep learning▼2gnd | |
| 650 | ▼aNatu?rliche Sprache▼2gnd | |
| 655 | ▼aElectronic books. | |
| 700 | ▼aHora, Shubhangi. | |
| 700 | ▼aJain, Tanuj. | |
| 700 | ▼aWambugu, Monicah. | |
| 776 | ▼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 | |
| 856 | ▼3EBSCOhost▼uhttp://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2159932 | |
| 938 | ▼aAskews and Holts Library Services▼bASKH▼nBDZ0040175075 | |
| 938 | ▼aProQuest Ebook Central▼bEBLB▼nEBL5789190 | |
| 938 | ▼aYBP Library Services▼bYANK▼n300608063 | |
| 938 | ▼aEBSCOhost▼bEBSC▼n2159932 | |
| 994 | ▼a92▼bN |
Deep Learning for Natural Language Processing :Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
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Title
Deep Learning for Natural Language Processing :Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
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Birmingham : Packt Publishing, Limited 2019.
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1 online resource (372 pages)
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Exercise 22: Application of a Simple CNN to a Reuters News Topic for Classification / 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.
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