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  1. Урок 1. 00:08:12
    Course Outline
  2. Урок 2. 00:06:05
    Meet Rubber Ducky! Your AI Course Assistant using RAG
  3. Урок 3. 00:04:05
    Overview: Fundamentals of Retrieval Systems
  4. Урок 4. 00:05:38
    Overview of Information Retrieval
  5. Урок 5. 00:07:21
    What is Tokenization?
  6. Урок 6. 00:06:13
    OpenAI Tokenizer
  7. Урок 7. 00:06:36
    Libraries and Data Handling for RAG
  8. Урок 8. 00:07:00
    Tokenization Techniques
  9. Урок 9. 00:09:36
    Preprocessing Steps
  10. Урок 10. 00:07:07
    Types of Retrieval Systems
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    Vector Space Model (TF-IDF)
  12. Урок 12. 00:07:52
    Implementing TF-IDF - Part 1
  13. Урок 13. 00:04:16
    Implementing TF-IDF - Part 2
  14. Урок 14. 00:04:58
    TF-IDF Function and Output Analysis
  15. Урок 15. 00:06:04
    Boolean Retrieval Model
  16. Урок 16. 00:06:56
    Preprocessing Steps - Part 2
  17. Урок 17. 00:06:05
    Setting a Directory
  18. Урок 18. 00:08:43
    Boolean Retrieval Implementation
  19. Урок 19. 00:07:15
    Probabilistic Retrieval Model
  20. Урок 20. 00:03:33
    Probabilistic Retrieval Model Setup
  21. Урок 21. 00:05:38
    Probabilistic Retrieval Model
  22. Урок 22. 00:11:30
    How Google Search Works
  23. Урок 23. 00:07:23
    Key Concepts: Indexing, Querying, and Ranking
  24. Урок 24. 00:02:32
    What Did You Learn in This Section?
  25. Урок 25. 00:11:53
    ReAct Prompt Engineering
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    Chain of Thought Prompt Engineering
  27. Урок 27. 00:01:47
    Overview: Generative AI Fundamentals
  28. Урок 28. 00:04:10
    Introduction to Text Generation
  29. Урок 29. 00:12:48
    Understanding Transformers
  30. Урок 30. 00:08:25
    Rock-Paper-Scissors, Dices and Strawberries
  31. Урок 31. 00:04:20
    Getting a Hugging Face Key
  32. Урок 32. 00:13:09
    Langchain and Hugging Face Setup
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    Basic Text Generation
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    Attention Mechanisms
  35. Урок 35. 00:06:23
    Understanding Generation Model Parameters
  36. Урок 36. 00:15:00
    System Message and Parameters
  37. Урок 37. 00:06:15
    Text Generation with System Message
  38. Урок 38. 00:10:49
    Text Generation with Parameters
  39. Урок 39. 00:04:52
    OpenAI Playground - top P
  40. Урок 40. 00:01:40
    What Did You Learn in This Section?
  41. Урок 41. 00:14:26
    LLMs, Few-shot, Scaling and Factuality
  42. Урок 42. 00:02:39
    Overview: RAG Fundamentals
  43. Урок 43. 00:05:18
    Introduction to RAG Architecture
  44. Урок 44. 00:06:30
    Hugging Face Setup
  45. Урок 45. 00:09:03
    Tokenization and Embeddings for RAG
  46. Урок 46. 00:04:16
    FAISS Index: Efficient Similarity Search
  47. Урок 47. 00:04:31
    Building a Retrieval System
  48. Урок 48. 00:07:42
    Developing a Generative Model
  49. Урок 49. 00:07:53
    Implementing the RAG System
  50. Урок 50. 00:03:10
    What Did You Learn in this Section?
  51. Урок 51. 00:16:41
    LongRAG and LightRAG
  52. Урок 52. 00:03:48
    Overview: Working with the OpenAI API
  53. Урок 53. 00:08:48
    OpenAI API for Text
  54. Урок 54. 00:05:50
    Setting Up OpenAI API Key
  55. Урок 55. 00:04:32
    OpenAI API Setup
  56. Урок 56. 00:07:03
    Generating Text with OpenAI API
  57. Урок 57. 00:10:21
    OpenAI API Parameters
  58. Урок 58. 00:08:15
    OpenAI API for Images
  59. Урок 59. 00:04:55
    With Image URL
  60. Урок 60. 00:03:50
    Converting Images to Base64
  61. Урок 61. 00:04:49
    Assess My Python Course Thumbnail
  62. Урок 62. 00:03:51
    What Did You Learn in this Section?
  63. Урок 63. 00:06:08
    Project Briefing: Customer Acquisition
  64. Урок 64. 00:05:40
    OpenAI Setup
  65. Урок 65. 00:08:22
    AI Agent System Prompt
  66. Урок 66. 00:05:25
    Processing Images for GenAI
  67. Урок 67. 00:13:39
    Extract Data with GenAI
  68. Урок 68. 00:06:19
    Improving GenAI Extraction
  69. Урок 69. 00:06:56
    GenAI with all Images
  70. Урок 70. 00:10:32
    PDF to Images
  71. Урок 71. 00:08:17
    Wrapping Up the OpenAI GenAI Project
  72. Урок 72. 00:04:35
    Overview: RAG with OpenAI GPT Models
  73. Урок 73. 00:04:58
    Case Study Briefing: Cooking Books
  74. Урок 74. 00:09:16
    Converting PDF to Images
  75. Урок 75. 00:12:04
    Reading a Single Image with GPT
  76. Урок 76. 00:09:11
    Enhancing AI with Prompt Engineering
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    Reading All Images in a Dataset
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    Filtering Non-relevant Information
  79. Урок 79. 00:06:51
    Understanding Embeddings in NLP
  80. Урок 80. 00:13:57
    Generating Embeddings
  81. Урок 81. 00:06:28
    Building FAISS Index and Metadata Integration
  82. Урок 82. 00:14:42
    Implementing a Robust Retrieval System
  83. Урок 83. 00:02:57
    Combining Outputs for Enhanced Results
  84. Урок 84. 00:11:43
    Constructing a Generative Model
  85. Урок 85. 00:06:42
    Complete RAG System Implementation
  86. Урок 86. 00:07:04
    How to Improve RAG Systems Effectively?
  87. Урок 87. 00:03:37
    Overview: Working With Unstructured Data
  88. Урок 88. 00:07:27
    Introduction to Langchain Library
  89. Урок 89. 00:06:42
    Excel Data: Best Practices for Data Handling
  90. Урок 90. 00:05:48
    Python - Initial Setup for Data Processing
  91. Урок 91. 00:05:14
    Loading Data and Implementing Chunking Strategies
  92. Урок 92. 00:06:11
    Developing a Retrieval System for Unstructured Data
  93. Урок 93. 00:09:13
    Building a Generation System for Dynamic Content
  94. Урок 94. 00:09:58
    Building Retrieval and Generation Functions
  95. Урок 95. 00:04:55
    Working with Word Documents
  96. Урок 96. 00:06:18
    Setting Up Word Documents for RAG
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    Implementing RAG for Word Documents
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    Working with PowerPoint Presentations
  99. Урок 99. 00:04:12
    PowerPoint Setup for RAG
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    RAG Implementation for PowerPoint
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    Working with EPUB Files
  102. Урок 102. 00:04:48
    EPUB Setup for RAG
  103. Урок 103. 00:02:23
    RAG Implementation for EPUB Files
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    Working with PDF Files
  105. Урок 105. 00:05:52
    PDF Setup for RAG
  106. Урок 106. 00:05:38
    RAG Implementation for PDF Files
  107. Урок 107. 00:03:57
    What Did You Learn in This Section?
  108. Урок 108. 00:02:57
    Exercise: Imposter Syndrome
  109. Урок 109. 00:03:39
    Overview: Multimodal RAG
  110. Урок 110. 00:05:59
    Introduction to Multimodal RAG
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    Setup and Video Processing
  112. Урок 112. 00:08:45
    Extracting Audio from Video
  113. Урок 113. 00:04:18
    Compressing Audio Files
  114. Урок 114. 00:10:08
    Transcribing Audio with OpenAI Whisper
  115. Урок 115. 00:06:32
    Whisper Model
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    Extracting Frames from Video
  117. Урок 117. 00:05:15
    Introduction to Contrastive Learning
  118. Урок 118. 00:05:23
    Understanding the CLIP Model
  119. Урок 119. 00:08:14
    Tokenizing Text for Multimodal Tasks
  120. Урок 120. 00:11:37
    Chunking and Embedding Text
  121. Урок 121. 00:08:37
    Embedding Images for Multimodal Analysis
  122. Урок 122. 00:06:47
    Understanding Cosine Similarity in Multimodal Contexts
  123. Урок 123. 00:10:27
    Applying Contrastive Learning and Cosine Similarity
  124. Урок 124. 00:11:12
    Visualizing Text and Image Embeddings
  125. Урок 125. 00:04:13
    Query Embedding Techniques
  126. Урок 126. 00:11:48
    Calculating Cosine Similarity for Query and Text
  127. Урок 127. 00:04:56
    GenAI Model Setup for Multimodal Tasks
  128. Урок 128. 00:07:12
    Building a GenAI Model
  129. Урок 129. 00:02:13
    What Did You Learn in This Section?
  130. Урок 130. 00:05:28
    Project Briefing: Starbucks Financial Data
  131. Урок 131. 00:11:23
    Transcribing Audio with OpenAI Whisper
  132. Урок 132. 00:07:36
    Embedding Transcription with CLIP
  133. Урок 133. 00:05:58
    Converting PDF to Images
  134. Урок 134. 00:04:59
    Embedding Images for Multimodal Analysis
  135. Урок 135. 00:17:14
    Retrieval System
  136. Урок 136. 00:05:00
    Preparing Context
  137. Урок 137. 00:12:47
    Generative System
  138. Урок 138. 00:08:32
    RAG with OpenAI File Search
  139. Урок 139. 00:05:53
    Vector Stores in OpenAI
  140. Урок 140. 00:05:47
    Setting a Vector Store in the OpenAI API
  141. Урок 141. 00:07:28
    Responses Endpoint with File Search
  142. Урок 142. 00:07:00
    RAG with GPT-4.1-mini
  143. Урок 143. 00:05:35
    RAG with System Developper / Messages
  144. Урок 144. 00:02:52
    Overview: Agentic RAG
  145. Урок 145. 00:07:52
    AI Agents
  146. Урок 146. 00:05:45
    Agentic RAG
  147. Урок 147. 00:09:55
    Setup and Data Loading
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    State Management and Memory in Agentic Systems
  149. Урок 149. 00:04:30
    AgentState Class
  150. Урок 150. 00:04:53
    Greeting the Customer
  151. Урок 151. 00:10:48
    AI Agent that Checks the Question
  152. Урок 152. 00:07:23
    AI Agent that Assesses the Validity of the question
  153. Урок 153. 00:05:47
    Retrieving the Documents
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    Testing the App
  155. Урок 155. 00:09:22
    Generate Answers
  156. Урок 156. 00:11:14
    AI Agent that Improves the Answer
  157. Урок 157. 00:05:30
    Asking User For More Questions
  158. Урок 158. 00:06:18
    Agentic RAG Recap - Key Learnings and Next Steps
  159. Урок 159. 00:02:20
    Game Plan for Knowledge Graphs with LightRAG
  160. Урок 160. 00:07:20
    Knowledge Graphs
  161. Урок 161. 00:08:50
    Knowledge Graphs vs Embeddings
  162. Урок 162. 00:07:36
    LightRAG Setup
  163. Урок 163. 00:04:41
    What is LightRAG?
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    Setting the Working Directory
  165. Урок 165. 00:04:49
    Data Prep
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    Naive RAG
  167. Урок 167. 00:06:11
    Implementing LightRAG
  168. Урок 168. 00:08:21
    Knowledge Graph Visualization
  169. Урок 169. 00:06:16
    Local Knowledge Graph Visualization
  170. Урок 170. 00:01:54
    Game Plan for RAGAS
  171. Урок 171. 00:06:14
    Assessing RAG with RAGAS
  172. Урок 172. 00:03:51
    RAGAS Setup
  173. Урок 173. 00:09:52
    Embedding and Facebook AI Similarity Search (FAISS)
  174. Урок 174. 00:11:37
    Python - RAG
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    Synthetic Data
  176. Урок 176. 00:04:59
    Generating Synthetic Data
  177. Урок 177. 00:06:29
    Python - Answering Synthetic Dataset
  178. Урок 178. 00:05:33
    ROUGE (Recall-Oriented Understudy for Gisting Evaluation) Score
  179. Урок 179. 00:13:50
    ROUGE
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    LLM-Based Assessment
  181. Урок 181. 00:05:36
    Simple Criteria Score - Part 1
  182. Урок 182. 00:05:40
    Simple Criteria Score - Part 2
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    Factual Correctness
  184. Урок 184. 00:04:53
    Rubrics Score
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    Semantic Similarity
  186. Урок 186. 00:05:17
    Factual Correctness
  187. Урок 187. 00:03:13
    Context Precision
  188. Урок 188. 00:04:58
    Semantic Similarity
  189. Урок 189. 00:03:12
    Context Recall
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    Context Precision
  191. Урок 191. 00:04:37
    Response Relevancy
  192. Урок 192. 00:04:56
    Context Recall
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    Response Relevancy
  194. Урок 194. 00:03:18
    Key Learnings and Outcomes: RAGAS
  195. Урок 195. 00:01:18
    Thank You!