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Урок 1.
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Course Outline
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Урок 2.
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Meet Rubber Ducky! Your AI Course Assistant using RAG
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Урок 3.
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Overview: Fundamentals of Retrieval Systems
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Урок 4.
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Overview of Information Retrieval
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Урок 5.
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What is Tokenization?
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Урок 6.
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OpenAI Tokenizer
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Урок 7.
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Libraries and Data Handling for RAG
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Урок 8.
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Tokenization Techniques
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Урок 9.
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Preprocessing Steps
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Урок 10.
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Types of Retrieval Systems
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Урок 11.
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Vector Space Model (TF-IDF)
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Урок 12.
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Implementing TF-IDF - Part 1
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Урок 13.
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Implementing TF-IDF - Part 2
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Урок 14.
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TF-IDF Function and Output Analysis
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Урок 15.
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Boolean Retrieval Model
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Урок 16.
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Preprocessing Steps - Part 2
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Урок 17.
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Setting a Directory
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Урок 18.
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Boolean Retrieval Implementation
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Урок 19.
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Probabilistic Retrieval Model
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Урок 20.
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Probabilistic Retrieval Model Setup
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Урок 21.
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Probabilistic Retrieval Model
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Урок 22.
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How Google Search Works
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Урок 23.
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Key Concepts: Indexing, Querying, and Ranking
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Урок 24.
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What Did You Learn in This Section?
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Урок 25.
00:11:53
ReAct Prompt Engineering
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Урок 26.
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Chain of Thought Prompt Engineering
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Урок 27.
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Overview: Generative AI Fundamentals
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Урок 28.
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Introduction to Text Generation
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Урок 29.
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Understanding Transformers
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Урок 30.
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Rock-Paper-Scissors, Dices and Strawberries
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Урок 31.
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Getting a Hugging Face Key
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Урок 32.
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Langchain and Hugging Face Setup
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Урок 33.
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Basic Text Generation
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Урок 34.
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Attention Mechanisms
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Урок 35.
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Understanding Generation Model Parameters
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Урок 36.
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System Message and Parameters
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Урок 37.
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Text Generation with System Message
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Урок 38.
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Text Generation with Parameters
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Урок 39.
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OpenAI Playground - top P
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Урок 40.
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What Did You Learn in This Section?
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Урок 41.
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LLMs, Few-shot, Scaling and Factuality
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Урок 42.
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Overview: RAG Fundamentals
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Урок 43.
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Introduction to RAG Architecture
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Урок 44.
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Hugging Face Setup
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Урок 45.
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Tokenization and Embeddings for RAG
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Урок 46.
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FAISS Index: Efficient Similarity Search
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Урок 47.
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Building a Retrieval System
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Урок 48.
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Developing a Generative Model
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Урок 49.
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Implementing the RAG System
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Урок 50.
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What Did You Learn in this Section?
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Урок 51.
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LongRAG and LightRAG
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Урок 52.
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Overview: Working with the OpenAI API
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Урок 53.
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OpenAI API for Text
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Урок 54.
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Setting Up OpenAI API Key
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Урок 55.
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OpenAI API Setup
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Урок 56.
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Generating Text with OpenAI API
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Урок 57.
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OpenAI API Parameters
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Урок 58.
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OpenAI API for Images
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Урок 59.
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With Image URL
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Урок 60.
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Converting Images to Base64
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Урок 61.
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Assess My Python Course Thumbnail
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Урок 62.
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What Did You Learn in this Section?
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Урок 63.
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Project Briefing: Customer Acquisition
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Урок 64.
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OpenAI Setup
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Урок 65.
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AI Agent System Prompt
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Урок 66.
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Processing Images for GenAI
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Урок 67.
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Extract Data with GenAI
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Урок 68.
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Improving GenAI Extraction
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Урок 69.
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GenAI with all Images
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Урок 70.
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PDF to Images
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Урок 71.
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Wrapping Up the OpenAI GenAI Project
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Урок 72.
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Overview: RAG with OpenAI GPT Models
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Урок 73.
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Case Study Briefing: Cooking Books
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Урок 74.
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Converting PDF to Images
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Урок 75.
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Reading a Single Image with GPT
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Урок 76.
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Enhancing AI with Prompt Engineering
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Урок 77.
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Reading All Images in a Dataset
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Урок 78.
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Filtering Non-relevant Information
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Урок 79.
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Understanding Embeddings in NLP
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Урок 80.
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Generating Embeddings
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Урок 81.
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Building FAISS Index and Metadata Integration
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Урок 82.
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Implementing a Robust Retrieval System
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Урок 83.
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Combining Outputs for Enhanced Results
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Урок 84.
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Constructing a Generative Model
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Урок 85.
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Complete RAG System Implementation
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Урок 86.
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How to Improve RAG Systems Effectively?
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Урок 87.
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Overview: Working With Unstructured Data
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Урок 88.
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Introduction to Langchain Library
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Урок 89.
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Excel Data: Best Practices for Data Handling
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Урок 90.
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Python - Initial Setup for Data Processing
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Урок 91.
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Loading Data and Implementing Chunking Strategies
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Урок 92.
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Developing a Retrieval System for Unstructured Data
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Урок 93.
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Building a Generation System for Dynamic Content
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Урок 94.
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Building Retrieval and Generation Functions
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Урок 95.
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Working with Word Documents
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Урок 96.
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Setting Up Word Documents for RAG
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Урок 97.
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Implementing RAG for Word Documents
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Урок 98.
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Working with PowerPoint Presentations
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Урок 99.
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PowerPoint Setup for RAG
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Урок 100.
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RAG Implementation for PowerPoint
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Урок 101.
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Working with EPUB Files
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Урок 102.
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EPUB Setup for RAG
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Урок 103.
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RAG Implementation for EPUB Files
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Урок 104.
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Working with PDF Files
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Урок 105.
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PDF Setup for RAG
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Урок 106.
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RAG Implementation for PDF Files
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Урок 107.
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What Did You Learn in This Section?
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Урок 108.
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Exercise: Imposter Syndrome
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Урок 109.
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Overview: Multimodal RAG
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Урок 110.
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Introduction to Multimodal RAG
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Урок 111.
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Setup and Video Processing
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Урок 112.
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Extracting Audio from Video
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Урок 113.
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Compressing Audio Files
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Урок 114.
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Transcribing Audio with OpenAI Whisper
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Урок 115.
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Whisper Model
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Урок 116.
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Extracting Frames from Video
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Урок 117.
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Introduction to Contrastive Learning
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Урок 118.
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Understanding the CLIP Model
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Урок 119.
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Tokenizing Text for Multimodal Tasks
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Урок 120.
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Chunking and Embedding Text
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Урок 121.
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Embedding Images for Multimodal Analysis
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Урок 122.
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Understanding Cosine Similarity in Multimodal Contexts
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Урок 123.
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Applying Contrastive Learning and Cosine Similarity
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Урок 124.
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Visualizing Text and Image Embeddings
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Урок 125.
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Query Embedding Techniques
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Урок 126.
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Calculating Cosine Similarity for Query and Text
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Урок 127.
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GenAI Model Setup for Multimodal Tasks
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Урок 128.
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Building a GenAI Model
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Урок 129.
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What Did You Learn in This Section?
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Урок 130.
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Project Briefing: Starbucks Financial Data
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Урок 131.
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Transcribing Audio with OpenAI Whisper
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Урок 132.
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Embedding Transcription with CLIP
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Урок 133.
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Converting PDF to Images
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Урок 134.
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Embedding Images for Multimodal Analysis
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Урок 135.
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Retrieval System
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Урок 136.
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Preparing Context
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Урок 137.
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Generative System
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Урок 138.
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RAG with OpenAI File Search
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Урок 139.
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Vector Stores in OpenAI
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Урок 140.
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Setting a Vector Store in the OpenAI API
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Урок 141.
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Responses Endpoint with File Search
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Урок 142.
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RAG with GPT-4.1-mini
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Урок 143.
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RAG with System Developper / Messages
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Урок 144.
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Overview: Agentic RAG
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Урок 145.
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AI Agents
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Урок 146.
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Agentic RAG
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Урок 147.
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Setup and Data Loading
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Урок 148.
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State Management and Memory in Agentic Systems
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Урок 149.
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AgentState Class
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Урок 150.
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Greeting the Customer
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Урок 151.
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AI Agent that Checks the Question
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Урок 152.
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AI Agent that Assesses the Validity of the question
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Урок 153.
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Retrieving the Documents
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Урок 154.
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Testing the App
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Урок 155.
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Generate Answers
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Урок 156.
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AI Agent that Improves the Answer
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Урок 157.
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Asking User For More Questions
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Урок 158.
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Agentic RAG Recap - Key Learnings and Next Steps
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Урок 159.
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Game Plan for Knowledge Graphs with LightRAG
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Урок 160.
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Knowledge Graphs
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Урок 161.
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Knowledge Graphs vs Embeddings
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Урок 162.
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LightRAG Setup
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Урок 163.
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What is LightRAG?
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Урок 164.
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Setting the Working Directory
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Урок 165.
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Data Prep
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Урок 166.
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Naive RAG
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Урок 167.
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Implementing LightRAG
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Урок 168.
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Knowledge Graph Visualization
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Урок 169.
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Local Knowledge Graph Visualization
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Урок 170.
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Game Plan for RAGAS
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Урок 171.
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Assessing RAG with RAGAS
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Урок 172.
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RAGAS Setup
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Урок 173.
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Embedding and Facebook AI Similarity Search (FAISS)
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Урок 174.
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Python - RAG
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Урок 175.
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Synthetic Data
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Урок 176.
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Generating Synthetic Data
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Урок 177.
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Python - Answering Synthetic Dataset
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Урок 178.
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ROUGE (Recall-Oriented Understudy for Gisting Evaluation) Score
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Урок 179.
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ROUGE
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Урок 180.
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LLM-Based Assessment
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Урок 181.
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Simple Criteria Score - Part 1
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Урок 182.
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Simple Criteria Score - Part 2
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Урок 183.
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Factual Correctness
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Урок 184.
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Rubrics Score
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Урок 185.
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Semantic Similarity
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Урок 186.
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Factual Correctness
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Урок 187.
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Context Precision
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Урок 188.
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Semantic Similarity
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Урок 189.
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Context Recall
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Урок 190.
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Context Precision
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Урок 191.
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Response Relevancy
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Урок 192.
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Context Recall
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Урок 193.
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Response Relevancy
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Урок 194.
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Key Learnings and Outcomes: RAGAS
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Урок 195.
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Thank You!