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Deep Learning for Natural Language Processing · LearnSpace
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courseraПрограммирование

Deep Learning for Natural Language Processing

Курс от University of Colorado Boulder
Средний≈ 21.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Deep learning has revolutionized the field of natural language processing and led to many state-of-the-art results. This course introduces students to neural network models and training algorithms frequently used in natural language processing. At the end of this course, learners will be able to explain and implement feedforward networks, recurrent neural networks, and transformers. They will also have an understanding of transfer learning and the inner workings of large language models. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

Навыки, которые вы освоите

Recurrent Neural Networks (RNNs)Natural Language ProcessingDeep LearningModel OptimizationFine-tuningLarge Language ModelingTransfer LearningEmbeddingsPrompt EngineeringModel TrainingGenerative Model ArchitecturesLLM ApplicationArtificial Neural Networks

Программа курса

4 модулей · 85 учебных материалов

01Feedforward Neural Nets and Recurrent Neural Networks30 материалов

Introduction to the Course

Course Updates and Accessibility SupportЧтениеEarn Academic Credit for Your Work! ЧтениеCourse SupportЧтениеAssessment ExpectationsЧтение

Учитесь у экспертов

Katharina von der Wense

Преподаватель курса

Deep Learning for Natural Language Processing
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 21.1 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Итальянский, Корейский, Немецкий, Индонезийский, Испанский, Японский, Казахский

Часть программы вашего университета
AI Citation and AcknowledgementЧтение

Feedforward Networks

The PerceptronВидеоThe XOR ProblemВидеоFeedforward NetworksВидеоFeedforward Networks in NLPВидеоMathematics of Feedforward NetworksЧтениеSentence Embeddings with Feedforward NetworksВидеоFeedforward NetworksЗадание

Recurrent Neural Networks

Sentence Embeddings with Recurrent Neural NetworksВидеоSequence Labelling with Recurrent Neural NetworksВидеоMathematics of Recurrent Neural NetworksЧтениеLSTMs and GRUsВидеоBidirectional Recurrent Neural NetworksВидеоHierarchical Recurrent Neural NetworksВидеоRecurrent Neural Networks in PythonВидеоRecurrent Neural NetworksЗадание

Implementing and Training Neural Networks

Loss FunctionsВидеоThe Intuition behind Gradient DescentВидеоStochastic Gradient DescentВидеоStochastic Gradient DescentЧтениеStochastic Gradient Descent in PythonВидеоStochastic Gradient DescentЛабораторнаяTraining Neural NetworksЗадание

Review and Exercises

AI Policy QuizЗаданиеFeedforward Networks, Recurrent Neural Networks, and How to Train ThemЗаданиеRecurrent Neural Networks for Sequence LabellingПрограммирование
02Sequence to Sequence Models, Attention, Transformers16 материалов

Recurrent Sequence-to-Sequence Models and the First Attention Mechanism

Sequence-to-sequence Tasks in NLPВидеоEarly Recurrent Sequence-to-Sequence ModelsВидеоAlignment in Machine TranslationВидеоSequence-to-Sequence Models with AttentionВидеоSequence-to-Sequence Models and AttentionЗадание

Transformer Models

The Transformer ModelВидеоMathematics of Multi-Head AttentionЧтениеApplications of the Transformer ModelВидеоVariants of the Attention MechanismВидеоTransformer ModelsЗадание

Tips and Tricks for Training Neural Networks

RegularizationВидеоOptimizersВидеоEfficient Model TrainingВидеоTips and Tricks for Training Neural NetworksЗадание

Review and Practice

Sequence-to-sequence Models, Attention, and TransformersЗаданиеTransformers for Sequence ClassificationПрограммирование
03Transfer Learning22 материалов

Pretraining, Finetuning, and Popular Pretrained Models

Low-Resource Settings in NLPВидеоPretraining & FinetuningВидеоPopular Pretrained Language Models: GPT&GPT-2ВидеоPopular Pretrained Masked Language Models: BERT&Co.ВидеоDomain AdaptationВидеоCatastrophic ForgettingВидеоPretraining & FinetuningЗадание

Multitask Training and Data Augmentation

What Is Multitask Training?ВидеоParameter Sharing for Multitask TrainingВидеоWeighing of LossesВидеоTask CombinationsВидеоData AugmentationВидеоPopular Multitask-Pretrained ModelsВидео

Crosslingual Transfer and Multilingual Pretrained Models

What Is Crosslingual Transfer?ВидеоTranslation-based Crosslingual Transfer ApproachesВидеоZero-shot, One-shot and Few-shot LearningВидеоOn Suitable Transfer LanguagesВидеоPopular Multilingual Pretrained ModelsВидеоCrosslingual Transfer and Multilingual Pretrained ModelsЗадание

Review and Practice

Transfer Learning ЗаданиеFinetuning Machine Translation ModelsПрограммирование
04Large Language Models17 материалов

Large Language Models and How to Use Them

Large Language Models and Emergent AbilitiesВидеоParameter-Efficient FinetuningВидеоPrompting, Prompt Engineering, and In-Context LearningВидеоPrompting ChallengesВидеоLarge Language Models and How to Use ThemЗадание

Language-and-Vision Models

The Limits of Language-only ModelsВидеоPopular Pretrained Language-and-Vision ModelsВидеоMultimodality Beyond VisionВидеоLanguage-and-Vision ModelsЗадание

Non-functional Properties of Large Language Models

HallucinationsВидеоFairness and Model AlignmentВидеоResource Efficiency and Knowledge DistillationВидеоPrivacyВидеоInterpretabilityВидеоNon-functional Properties of Large Language ModelsЗадание

Review and Practice

Large Language ModelsЗаданиеLarge Language ModelsПрограммирование
Multitask Training and Data AugmentationЗадание