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Foundations of LLMs and Deep Learning for Text Analysis · LearnSpace
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Foundations of LLMs and Deep Learning for Text Analysis

Курс от Packt
Средний≈ 3.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course introduces the foundational concepts of large language models (LLMs) and deep learning techniques for text analysis, a critical skill set in today’s AI-driven landscape. As organizations increasingly rely on intelligent systems to process and interpret language data, understanding these technologies has become essential for modern professionals. Throughout the course, learners will explore how deep learning models analyze and extract meaning from textual data, gaining practical insights into real-world NLP applications. By studying the architecture and working principles of transformers and LLMs, participants will build the skills needed to apply these technologies to tasks such as text classification, sentiment analysis, and language generation. What sets this course apart is its balance of conceptual clarity and application-focused learning, combining theoretical foundations with examples drawn from modern AI systems. Learners will gain a clear understanding of how cutting-edge models power today’s most advanced language technologies. This course is ideal for aspiring data scientists, AI practitioners, and developers with a basic understanding of programming and machine learning concepts who want to deepen their expertise in NLP and deep learning. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Навыки, которые вы освоите

Prompt EngineeringLarge Language ModelingGenerative Model ArchitecturesGenerative AIEmbeddingsText MiningMachine LearningNatural Language ProcessingFine-tuningDeep LearningData EthicsMultimodal PromptsLLM ApplicationArtificial Neural NetworksRecurrent Neural Networks (RNNs)

Программа курса

3 модулей · 29 учебных материалов

01Analyzing Text Data with Deep Learning11 материалов

From Words to Insights: Deep Learning Approaches for Text Analysis

OverviewВидеоIntroductionЧтениеOne-hot EncodingЧтениеRepresenting Text for Machine LearningЗадание

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Packt - Course Instructors

Преподаватель курса

Foundations of LLMs and Deep Learning for Text Analysis
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 3.1 ч

3 модулей

Язык: Английский

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

Часть программы вашего университета
TF-IDFЧтение
Word2VecЧтение
A Notion of Similarity for TextЧтение
RNNsЧтение
GRUsЧтение
Performing Sentiment Analysis with Embedding and Deep LearningЧтение
Exploring Text Data Analysis ConceptsЗадание
02The Transformer: The Model Behind the Modern AI Revolution9 материалов

Unpacking Transformers: From Attention to Real-World Applications

OverviewВидеоIntroductionЧтениеIntroducing the Transformer ModelЧтениеFrom Attention to the Transformer ArchitectureЗаданиеTraining a TransformerЧтениеExploring Masked Language ModelingЧтениеVisualizing Internal MechanismsЧтениеApplying a TransformerЧтениеExploring the Transformer ModelЗадание
03Exploring LLMs as a Powerful AI Engine9 материалов

Unveiling the Power and Challenges of Modern Language Models

OverviewВидеоIntroductionЧтениеEmergent PropertiesЧтениеInstruction Tuning, Fine-Tuning, and AlignmentЧтениеExploring Smaller and More Efficient LLMsЧтениеExploring Multimodal ModelsЧтениеUnderstanding Hallucinations and Ethical and Legal IssuesЧтениеPrompt EngineeringЧтениеExploring the Capabilities and Challenges of Large Language ModelsЗадание