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Hands-On Python Natural Language Processing
NaturalLanguageProcessing(NLP)isthesubfieldincomputationallinguisticsthatenablescomputerstounderstand,process,andanalyzetext.Thisbookcaterstotheunmetdemandforhands-ontrainingofNLPconceptsandprovidesexposuretoreal-worldapplicationsalongwithasolidtheoreticalgrounding.ThisbookstartsbyintroducingyoutothefieldofNLPanditsapplications,alongwiththemodernPythonlibrariesthatyou'llusetobuildyourNLP-poweredapps.Withthehelpofpracticalexamples,you’lllearnhowtobuildreasonablysophisticatedNLPapplications,andcovervariousmethodologiesandchallengesindeployingNLPapplicationsintherealworld.You'llcoverkeyNLPtaskssuchastextclassification,semanticembedding,sentimentanalysis,machinetranslation,anddevelopingachatbotusingmachinelearninganddeeplearningtechniques.Thebookwillalsohelpyoudiscoverhowmachinelearningtechniquesplayavitalroleinmakingyourlinguisticappssmart.Everychapterisaccompaniedbyexamplesofreal-worldapplicationstohelpyoubuildimpressiveNLPapplicationsofyourown.BytheendofthisNLPbook,you’llbeabletoworkwithlanguagedata,usemachinelearningtoidentifypatternsintext,andgetacquaintedwiththeadvancementsinNLP.
目錄(90章)
倒序
- 封面
- 版權信息
- About Packt
- Why subscribe?
- About the authors
- Preface
- Section 1: Introduction
- Understanding the Basics of NLP
- Programming languages versus natural languages
- Why should I learn NLP?
- Current applications of NLP
- Summary
- NLP Using Python
- Technical requirements
- Understanding Python with NLP
- Important Python libraries
- Web scraping libraries and methodology
- Overview of Jupyter Notebook
- Summary
- Section 2: Natural Language Representation and Mathematics
- Building Your NLP Vocabulary
- Technical requirements
- Lexicons
- Phonemes graphemes and morphemes
- Tokenization
- Understanding word normalization
- Summary
- Transforming Text into Data Structures
- Technical requirements
- Understanding vectors and matrices
- Exploring the Bag-of-Words architecture
- TF-IDF vectors
- Distance/similarity calculation between document vectors
- One-hot vectorization
- Building a basic chatbot
- Summary
- Word Embeddings and Distance Measurements for Text
- Technical requirements
- Understanding word embeddings
- Demystifying Word2vec
- Training a Word2vec model
- Word mover’s distance
- Summary
- Exploring Sentence- Document- and Character-Level Embeddings
- Technical requirements
- Venturing into Doc2Vec
- Exploring fastText
- Understanding Sent2Vec and the Universal Sentence Encoder
- Summary
- Section 3: NLP and Learning
- Identifying Patterns in Text Using Machine Learning
- Technical requirements
- Introduction to ML
- Data preprocessing
- The Naive Bayes algorithm
- The SVM algorithm
- Productionizing a trained sentiment analyzer
- Summary
- From Human Neurons to Artificial Neurons for Understanding Text
- Technical requirements
- Exploring the biology behind neural networks
- How does a neural network learn?
- Understanding regularization
- Let's talk Keras
- Building a question classifier using neural networks
- Summary
- Applying Convolutions to Text
- Technical requirements
- What is a CNN?
- Detecting sarcasm in text using CNNs
- Summary
- Capturing Temporal Relationships in Text
- Technical requirements
- Baby steps toward understanding RNNs
- Vanishing and exploding gradients
- Architectural forms of RNNs
- Giving memory to our networks – LSTMs
- Building a text generator using LSTMs
- Exploring memory-based variants of the RNN architecture
- Summary
- State of the Art in NLP
- Technical requirements
- Seq2Seq modeling
- Translating between languages using Seq2Seq modeling
- Let's pay some attention
- Transformers
- BERT
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-18 18:29:09
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