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| 001 | | 000000316286 |
| 003 | | OCoLC |
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| 008 | | 190713s2019 enk o 000 0 eng d |
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▼a 019446113
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▼a 1104692494 |
| 020 | |
▼a 1838553673 |
| 020 | |
▼a 9781838553678
▼q (electronic bk.) |
| 035 | |
▼a 2159932
▼b (N$T) |
| 035 | |
▼a (OCoLC)1107580393
▼z (OCoLC)1104692494 |
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▼a 9781838553678
▼b Packt Publishing |
| 037 | |
▼a 9BD5685A-365D-4013-9C82-4F86557B527A
▼b OverDrive, Inc.
▼n http://www.overdrive.com |
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▼a EBLCP
▼b eng
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▼d UKMGB
▼d OCLCO
▼d OCLCF
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▼d OCLCQ
▼d YDX
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▼d TEFOD
▼d OCLCQ
▼d N$T
▼d 248032 |
| 049 | |
▼a MAIN |
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▼a QA76.9.N38
▼b .B655 2019 |
| 082 | 04 |
▼a 006.35
▼2 23 |
| 100 | 1 |
▼a Reddy Bokka, Karthiek. |
| 245 | 10 |
▼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 |
| 500 | |
▼a Exercise 22: Application of a Simple CNN to a Reuters News Topic for Classification |
| 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 |
| 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. |
| 588 | 0 |
▼a Print version record. |
| 590 | |
▼a Added to collection customer.56279.3 |
| 650 | 0 |
▼a Natural language processing (Computer science) |
| 650 | 0 |
▼a Neural networks (Computer science) |
| 650 | 0 |
▼a Machine learning. |
| 650 | 7 |
▼a Machine learning.
▼2 fast
▼0 (OCoLC)fst01004795 |
| 650 | 7 |
▼a Natural language processing (Computer science)
▼2 fast
▼0 (OCoLC)fst01034365 |
| 650 | 7 |
▼a Neural networks (Computer science)
▼2 fast
▼0 (OCoLC)fst01036260 |
| 650 | 7 |
▼a Deep learning
▼2 gnd |
| 650 | 7 |
▼a Natu?rliche Sprache
▼2 gnd |
| 655 | 4 |
▼a Electronic books. |
| 700 | 1 |
▼a Hora, Shubhangi. |
| 700 | 1 |
▼a Jain, Tanuj. |
| 700 | 1 |
▼a Wambugu, Monicah. |
| 776 | 08 |
▼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, 짤2019
▼z 9781838550295 |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2159932 |
| 938 | |
▼a Askews and Holts Library Services
▼b ASKH
▼n BDZ0040175075 |
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▼a ProQuest Ebook Central
▼b EBLB
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