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Applied Natural Language Processing in Engineering Part 1 · LearnSpace
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Applied Natural Language Processing in Engineering Part 1

Курс от Northeastern University
Уровень не указан≈ 27.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Welcome to this course on applied natural language processing in engineering. This course is designed to provide you with an in-depth understanding of NLP, a pivotal area of artificial intelligence that empowers computers to comprehend, interpret, and generate human language. Throughout this course, you will explore a wide range of topics, from fundamental NLP tasks like text classification and Named Entity Recognition (NER) to advanced techniques in neural machine translation and optimization methods critical for machine learning. We will delve into the complexities of teaching language to machines, addressing challenges like ambiguity, grammar, and cultural nuances. By the end of this part 1 course, you will have a foundational understanding of how modern NLP systems work - particularly those involving machine learning and deep learning. These topics will equip you to build, analyze and improve NLP systems across many applications.

Навыки, которые вы освоите

Natural Language ProcessingEmbeddingsModel OptimizationMachine Learning MethodsDependency AnalysisDeep LearningModel EvaluationMachine LearningArtificial Intelligence and Machine Learning (AI/ML)Artificial Neural NetworksSupervised Learning

Программа курса

7 модулей · 176 учебных материалов

01Introduction to Speech, Language, and Natural Language Processing26 материалов

Getting Started

Course IntroductionЧтениеCourse IntroductionВидеоMeet Your FacultyВидеоSyllabus - Applied Natural Language Processing in Engineering Part 1Чтение

Учитесь у экспертов

Ramin Mohammadi

Преподаватель курса

Applied Natural Language Processing in Engineering Part 1
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 27.8 ч

7 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Венгерский

Часть программы вашего университета
Academic IntegrityЧтение

Linear Algebra - Pre-work

Recommended Prior KnowledgeЧтение

Lesson 1: What is NLP?

Week 1 IntroductionЧтениеIntroduction to NLPЧтениеNatural Language Processing (NLP)ВидеоExample: ChatbotsЧтениеExample: Email FilteringЧтениеExample: Sentiment AnalysisЧтениеExample: GPT - 3ЧтениеExample: ChatGPT CapabilitiesЧтениеAssess Your Learning: What is NLP?Задание

Lesson 2: Motivation

Natural Language ProcessingЧтениеFunny Takes on Language EvolutionЧтениеChallenges of Teaching Language to AIВнешний инструментHow Do We Represent the Meaning of a Word?ЧтениеRepresenting the Meaning of a WordВидеоHow Do We Have Usable Meaning in a Computer?ЧтениеWords as Discrete SymbolsЧтениеRepresenting Words by Their ContextЧтениеWord VectorsЧтениеFinal Thoughts on NLPЧтениеAssess Your Learning: MotivationЗадание
02Gradient Descent & Optimization Techniques21 материалов

Lesson 1: How Machine Learning Enables NLP

Week 2 OverviewЧтениеMachine LearningЧтениеMachine Learning and NLPВидеоVariations of Gradient DescentЧтениеTypes of ML in NLPЧтениеWhat is a Model in NLP and How Does it Learn?ЧтениеUnderstanding Cost FunctionsЧтениеMinimizing the Cost Function in NLPЧтениеWhy Optimization Techniques MatterЧтениеWhy SGD WorksЧтениеAssess Your Learning: ML in NLPЗадание

Lesson 2: Optimization Techniques

Optimization TechniquesВидеоJacobian Matrix & Hessian MatrixЧтениеMomentumЧтениеNewton's MethodsЧтениеQuasi-Newton MethodsЧтениеRoot Mean Square Propagation (RMSProp)ЧтениеAdaptive Moment Estimation (Adam)

Module 2 Assessment

Module 2 QuizЗадание
03Neural Networks & Cost Functions20 материалов

Lesson 1: Named Entity Recognition (NER) & Neural Networks

Week 3 OverviewЧтениеNeural NetworksЧтениеNamed Entity Recognition (NER)ЧтениеNeural Networks DefinitionsВидеоNER as a Binary Regression ProblemЧтениеNeural NetworkЧтениеSome Common Activation FunctionsВнешний инструментNeural Network StructureЧтениеHow Does a Neural Network Learn?ЧтениеNetwork PropagationВидеоMathematical RepresentationЧтениеSteps in Back Propagation AlgorithmЧтениеStochastic GradientЧтениеAssess Your Learning: NER & Neural NetworksЗадание

Lesson 2: Common Cost Functions

Classification TasksЧтениеSequence-to-Sequence TasksЧтениеSequence Labeling TasksЧтениеRegression Tasks & Divergence MeasuresЧтениеAssess Your Learning: Cost FunctionsЗадание

Module 3 Assessment

Module 3 QuizЗадание
04Embeddings, GloVe, Evaluation Techniques37 материалов

Lesson 1: GloVe

Week 4 OverviewЧтениеIntroduction to GLoVeЧтениеCo-occurrence MatrixЧтениеObjective: Ratio of Co-occurrencesЧтениеCalculating Probability RatiosЧтениеSymmetry and Linearity in GloVeЧтениеMinimizing the Cost Function and Optimizing Word VectorsЧтениеOptimization ProcessЧтениеFinal Word VectorsЧтениеImplicit Properties in GloVeЧтениеGloVe Training ProcessВнешний инструментGLoVe IntroductionЧтениеGLoVe Training ProcessВидеоAssess Your Learning: GLoVeЗадание

Lesson 2: Word2Vec

Word2VecВидеоWhat is Language Modeling?ЧтениеCo-occurrence MatrixЧтениеVector Representations for WordЧтениеContinuous Bag of Words (CBOW)ЧтениеMathematical ObjectivesЧтениеMathematical Objectives 2

Lesson 3: Skip-Gram

Skip-GramЧтениеGradient DerivationЧтениеThe Challenge of Training Skip-GramЧтениеBinary Classification PerspectiveЧтениеSkip-GramВидеоGradient of Negative Sampling ObjectiveЧтениеConnecting Between Skip-Gram, Negative Sampling, and One Sampling

Worked Examples

Word2Vec ExampleЧтениеWord2Vec Worked Example ЧтениеWord2Vec Example 2Чтение

Module 4 Assessment

Module 4 QuizЗадание
05Evaluation Techniques12 материалов

Lesson 1: NLP Model Evaluation

Week 5 OverviewЧтениеEvaluation TechniquesВнешний инструментGeneral Concept of Evaluation (in NLP)ЧтениеKey Differences Between Intrinsic and Extrinsic EvaluationЧтениеCross-Entropy Loss - IntrinsicЧтениеCross-Entropy and Learning from Incorrect PredictionsЧтениеPerplexity - IntrinsicЧтениеBilingual Evaluation Understudy Score (BLEU) - ExtrinsicЧтениеRecall and Precision in Text Summarization or TranslationЧтениеRecall-Oriented Understudy for Gisting Evaluation (ROUGE) - ExtrinsicЧтениеAssess Your Learning: NLP Model EvaluationЗадание

Module 5 Assessment

Module 5 QuizЗадание
06Topic Modeling22 материалов

Lesson 1: Matrix Factorization and Latent Semantic Analysis (LSA)

Week 6 OverviewЧтениеMatrix FactorizationЧтениеLatent Semantic Analysis (LSA)ЧтениеLSA ExampleЧтениеTopic Modeling Using Latent Semantic Analysis (LSA)ЧтениеTopic ModelingВидеоDimensions and ApplicationsЧтениеAssess Your Learning: Latent Semantic AnalysisЗадание

Lesson 2: Non-Negative Matrix Factorization (NMF)

Non-Negative Matrix Factorization (NMF)ЧтениеOperationalizing NMFЧтениеNumerical Example of NMFЧтениеApplications of NMFЧтениеAssess Your Learning: Non-Negative Matrix FactorizationЗадание

Lesson 3: Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA)ЧтениеDefining the Problem and Key AssumptionsЧтениеMathematical Model of LDAЧтениеSteps in LDA: Mathematical ExplanationЧтениеMaximizing the Posterior Probability in LDAЧтениеAssess Your Learning: Latent Dirichlet AllocationЗадание

Lesson 4: Putting it All Together

Recapping NMF & LDAВнешний инструментFull ExampleЧтение

Module 6 Assessment

Module 6 QuizЗадание
07Dependency Parsing38 материалов

Lesson 1: What is a Constituent?

Week 7 OverviewЧтениеIntroduction to Dependency ParsingЧтениеWhat is a Constituent?ЧтениеPseudoclefting & Sentence FragmentsЧтение

Lesson 2: Detection of Sentence Parts

Substitution of Well-formed SentencesЧтениеNaming the Other PhasesЧтениеImmediate Domination, Domination, & Feminine Kinship RelationsЧтениеAssess Your Learning: Constituents & Detection of Sentence PartsЗадание

Lesson 3: Phrase Structure (Constituency-Based Grammar)

Syntactic Parsing - Phrase StructureЧтениеTransition-Based and Graph Parsing ExamplesВидеоPhrase StructureЧтениеPhrase Structure Rules & ExplanationЧтениеBuilding the TreeЧтениеSummaryЧтение

Lesson 4: Dependency Structure

Dependency StructureЧтениеKey Concepts & ExampleЧтениеAdvantages of Dependency StructureЧтениеAssess Your Learning: Phrase and Dependency StructureЗадание

Lesson 5: Transition-Based Parsing

What is Transition-based Parsing?ЧтениеThe Greedy Transition-Based MethodЧтениеType 2: Shift-Reduce ParserЧтение

Lesson 6: Neural Transition-Based Parsing

IntroductionЧтениеNeural Advancements in Parsing: Dependency and SemanticsВидеоKey Concepts & ConfigurationЧтениеAdvantages, Challenges & ConsiderationsЧтение

Lesson 7: Graph-Based Dependency Parsing

IntroductionЧтениеKey Concepts of Graph-Based Dependency ParsingЧтениеWorked ExampleЧтениеAdvantages and DisadvantagesЧтение

Lesson 8: Transition-Based vs Graph-Based Parsing

Transition-Based vs Graph-Based ParsingЧтение

Lesson 9: Semantic Parsing

Semantic ParsingЧтениеAssess Your Learning: ParsingЗадание

Module 7 Examples

Example 1ЧтениеExample 2ЧтениеExample 3ЧтениеExample 4Чтение

Module 7 Assessment

Module 7 QuizЗадание

Course Wrap-Up

Congratulations! Чтение
Чтение
Overall Challenges of Second-Order Optimization TechniquesЧтение
Assess Your Learning: Optimization TechniquesЗадание
Чтение
Limitations of CBOWЧтение
Assess Your Learning: Word2Vec & CBOWЗадание
Чтение
Skip-Gram with Negative Sampling Across All WordsЧтение
Negative Sampling in Skip-Gram ModelЧтение
Assess Your Learning: Skip-Gram & Negative SamplingЗадание