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Getting Started with Generative AI · LearnSpace
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Getting Started with Generative AI

Курс от Edureka
Начальный≈ 19 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course introduces the foundational concepts and advanced techniques in Generative AI, covering key topics such as model architectures, data preparation, prompt engineering, and deployment strategies. Learners will gain practical experience with cutting-edge tools and methodologies to effectively design, fine-tune, and deploy generative AI solutions. By the end of this course, you will be able to: - Define the core principles of generative AI, including models, algorithms, and applications. - Apply data pre-processing and vectorization techniques to enhance generative AI models. - Evaluate the strengths and weaknesses of GANs, autoencoders, transformers, and LLMs. - Analyze and optimize prompting techniques for improved model performance. - Design evaluation methods using metrics like BLEU and ROUGE to assess model outputs. This course is suitable for the aspiring AI practitioners, software developers, data scientists, and ML engineers who want to enhance their skills in building, deploying, and optimizing generative AI solutions. Join us to establish a solid foundation in generative AI and take your career to the next level with hands-on expertise in this transformative technology!

Навыки, которые вы освоите

Generative AILarge Language ModelingPrompt EngineeringGenerative Adversarial Networks (GANs)Data PreprocessingFine-tuningResponsible AIOpen Source TechnologyData ProcessingModel OptimizationMachine LearningDeep LearningEmbeddingsData CleansingDatabase SystemsLLM ApplicationAI PersonalizationData VisualizationGenerative Model ArchitecturesArtificial Intelligence and Machine Learning (AI/ML)

Программа курса

5 модулей · 126 учебных материалов

01Foundations of Generative AI34 материалов

Understanding Generative AI

Specialization IntroductionВидеоCourse IntroductionВидеоCourse OverviewЧтениеWhat do You Already Know about Generative AI?DIALOGUE

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Edureka

Преподаватель курса

Getting Started with Generative AI
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Обучение на Coursera

≈ 19 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Defining Generative AI and its EvolutionВидео
Generative Vs. Discriminative ModelsВидео
Applications of Generative AIВидео
Understanding Generative vs. Discriminative ModelsDIALOGUE
The Progression of AI Models: From Symbolic to GenerativeЧтение
Generative AI Pipeline OverviewВидео
Introduce YourselfОбсуждение
Practice Quiz: Understanding Generative AIЗадание

Data Preparation and Vectorization

Importance of Data Pre-processingВидеоNavigating to Data Pre-processingВидеоTechniques for Data CleaningВидеоData Vectorization MethodsВидеоVectorization Techniques: TF-IDF, Word2Vec, and BeyondЧтениеApplications of Vectorization ВидеоDemonstration: Text Vectorization with TF-IDF and CountVectorizerВидеоData Quality Vs. Model ComplexityОбсуждениеPractice Quiz: Data Preparation and VectorizationЗадание

Autoencoders and GANs

Autoencoder Basics: Architecture and TrainingВидеоVariational Autoencoders: Overview and ApplicationsВидеоApplications of VAEs in Real-World AI ProblemsЧтениеUnderstanding Generative Adversarial NetworksВидеоDemonstration: Building a Simple Autoencoder in KerasВидеоTraining GANs: Key ConsiderationsВидеоVariants of GANs: DCGAN, CycleGANВидеоGANs Explained: From Zero to DCGAN and CycleGANЧтениеLatent Space in VAEsОбсуждениеPractice Quiz: Autoencoders and GANsЗадание

Module Wrap-Up and Assessment

Module 1 Checkpoint: Assessing Your Understanding of Generative AIDIALOGUEModule Summary: Foundations of Generative AIЧтениеKnowledge Check: Foundations of Generative AIЗадание
02Transformer Models and Large Language Models (LLMs)27 материалов

Transformers and Attention Mechanism

Evolution of Transformer ModelsВидеоAttention Mechanism and its Role in TransformersВидеоDemonstration: Visualizing Attention in Transformer ModelВидеоFrom Attention to Transformers: How Sequence Modeling EvolvedЧтениеOverview of BERTВидеоOverview of GPTВидеоGPT’s Generative PowerОбсуждениеPractice Quiz: Transformers and Attention MechanismЗадание

Exploring Large Language Models (LLMs)

Introduction to LLMsВидеоThe Rise of Instruction-Tuned LLMs: A New Paradigm in NLPЧтениеExploring Meta Llama 3 and Other Leading LLMsВидеоUse Cases of LLMs in Various DomainsВидеоUnderstanding LLM APIs and Their TypesВидеоAPI Integration with LLMsВидео

Open-Source LLM Ecosystem

Overview of Open-Source LLMsВидеоEthical Considerations in Deploying Open-Source ModelsВидеоComparing Open Source ModelsВидеоDemonstration: Hugging Face EcosystemВидеоEthical Risk Landscape in Open-Source AI ModelsЧтениеResponsible AI at ScaleОбсуждение

Module Wrap-Up and Assessment

Module 2 Checkpoint: Testing Your Understanding of Transformer Models and LLMsDIALOGUEModule Summary: Transformer Models and Large Language Models (LLMs)ЧтениеKnowledge Check: Transformer Models and Large Language Models (LLMs)Задание
03Generative AI Techniques and Tools33 материалов

Principles of Prompt Engineering

What is Prompt Engineering?ВидеоPrompt Design Strategies for Generative AI ModelsВидеоElements of an Effective PromptВидеоThe Science of Prompting: A Cognitive Perspective on PromptsЧтениеDemonstration: Crafting Zero-Shot, One-Shot, and Few-Shot PromptsВидеоDemonstration: Designing Creative Prompts for Diverse NLP TasksВидеоHuman Thinking, Machine ResponsesОбсуждениеPractice Quiz: Principles of Prompt EngineeringЗадание

Advanced Prompting Techniques

Few-shot and Zero-shot Learning StrategiesВидеоChain-of-Thought PromptingВидеоOptimizing Prompts for Specific OutputsВидеоDemonstration: Effective Prompt Design StrategiesВидеоPrompting with PurposeОбсуждениеPractice Quiz: Advanced Prompting TechniquesЗадание

Vector Databases and RAG

Introduction to Vector DatabasesВидеоHow Vector Databases Power Semantic Search in AIЧтениеChromaDB, Pinecone, WeaviateВидеоSemantic Search with Vector EmbeddingsВидеоRAG Architecture and Retriever TechniquesВидеоRAG in the Wild: Case Studies from Enterprise ApplicationsЧтение

Frameworks for Application Development

LangChain Architecture and ComponentsВидеоUsing Prompt Templates and ToolsВидеоLangChain in Action: Building Modular AI ApplicationsЧтениеFrom Pieces to ProductsОбсуждениеPractice Quiz: Frameworks for Application DevelopmentЗадание

Module Wrap-Up and Assessment

Module 3 Checkpoint: Assessing Your Mastery of Prompt Engineering and AI ToolsDIALOGUEModule Summary: Generative AI Techniques and ToolsЧтениеKnowledge Check: Generative AI Techniques and ToolsЗадание
04Fine-Tuning and Optimization of Generative Models25 материалов

Fine-Tuning Techniques

Fine-Tuning Fundamentals for Generative ModelsВидеоData Augmentation and Hyperparameter TuningВидеоFine-Tuning vs Pretraining: When and Why to Choose EachЧтениеBoosting with More DataОбсуждениеPractice Quiz: Fine-Tuning TechniquesЗадание

Advanced Prompting Techniques

Parameter-Efficient Fine-TuningВидеоLow-Rank AdaptationВидеоQuantized Low-Rank AdaptationВидеоQuantization in LLMs: Making Large Models Work on Small DevicesЧтениеFine-Tuning in Tight SpacesОбсуждениеPractice Quiz: Advanced Prompting TechniquesЗадание

Evaluating Generative Models

Challenges in Evaluating Generative AI ModelsВидеоEvaluation Metrics: BLEU, ROUGE, Inception ScoreВидеоQualitative versus Quantitative EvaluationВидеоDemonstration: Evaluating Text Generation with BLEU and ROUGEВидеоMeasuring the ImmeasurableОбсуждениеPractice Quiz: Evaluating Generative ModelsЗадание

Building and Deploying Generative AI Solutions

Introduction to LLMOpsВидеоThe Emergence of LLMOps: Best Practices from Industry LeadersЧтениеDeployment Strategies for Generative AI ApplicationsВидеоFrom Lab to LiveОбсуждениеPractice Quiz: Building and Deploying Generative AI SolutionsЗадание

Module Wrap-Up and Assessment

Module 4 Checkpoint: Fine-Tuning and Optimizing Generative ModelsDIALOGUEModule Summary: Fine-Tuning and Optimization of Generative ModelsЧтениеKnowledge Check: Fine-Tuning and Optimization of Generative ModelsЗадание
05Course Wrap-Up and Assessment7 материалов

Course Wrap-Up and Assessment

Practice Activity: Text Vectorization and AutoencoderЛабораторнаяPractice Activity: BLEU and ROUGE EvaluationЛабораторнаяPractice Project: AI-Powered Content Strategy SystemЧтениеEnd Course Knowledge Check: Getting Started with Generative AIЗаданиеDesigning a Generative AI-Based Text Generation and Application SystemЗаданиеCourse SummaryВидеоDescribe Your Learning JourneyОбсуждение
Demonstration: LLM Integration with Gemini APIВидео
Instruction-Tuned ModelsОбсуждение
Practice Quiz: Exploring Large Language Models (LLMs)Задание
Practice Quiz: Open-Source LLM EcosystemЗадание
Modular and Multimodal RAGВидео
Demonstration: ChromaDB: Installing, Creating and Managing Vector DatabaseВидео
Demonstration: Semantic Search with Vector EmbeddingsВидео
Retrieval Meets GenerationОбсуждение
Practice Quiz: Vector Databases and RAGЗадание