| 1. | What is Data Privacy | 0 | |
|
| 2. | Meet Claire: The AI That Turns Ideas Into Ready‑to‑Build Specs | 9 | |
|
| 3. | How Can AI Agents Help Humans Operate at Peak Cognitive Efficiency in Complex Workflows? | 1 | |
|
| 4. | Why Do We Need Sherpa | 1 | |
|
| 5. | From Start to Finish: Setting up a RAG System on Amazon Bedrock | 1 | |
|
| 6. | ReferWell - Helping Patients Find Specialists - Multi-agent LLM Systems Bootcamp | 3 | |
|
| 7. | Evaluating Agent Responses with LLMs | 0 | |
|
| 8. | Meet Aphrodite Oracle: An AI that Reads Academic Sources So You Don’t Have To | 1 | |
|
| 9. | When Should We Use Sherpa? | 1 | |
|
| 10. | Teachers Are Burning Out. We Built an AI to Help. | 4 | |
|
| 11. | AI Agents & Game Development: Why ChatGPT Isn’t Enough for D&D (And What I Built Instead) | 4 | |
|
| 12. | Intro to Llama-agents Framework (+ live demo) | 69 | |
|
| 13. | LLM Products and Entrepreneurship | 4 | |
|
| 14. | This AI Tracks Your Finances, Answers Complex Money Questions & Plans Your Goals | 7 | |
|
| 15. | Built Multi-agent LLM Products - Bootcamp Teaser | 5 | |
|
| 16. | Deduplication in DeepSeek R1 | 0 | |
|
| 17. | I Built an AI Coach that Analyzes Your Job Interviews Like a Real Coach | 6 | |
|
| 18. | Key Learnings from Building AI Agents: How Open Source Shaped Our Architecture | 9 | |
|
| 19. | How to Set Up a Workflow in Dify in Two Minutes | 0 | |
|
| 20. | How I built Adept Reader: An AI Tool that Makes Research Papers Easy for Product Managers | 4 | |
|
| 21. | G-DIVE: Geoscience Document Intelligence via Verifiable Extraction | 1 | |
|
| 22. | Data Stores, Prompt Repositories, and Memory Management | 1 | |
|
| 23. | Evaluating Agent's Responses | 0 | |
|
| 24. | The AI Nutritionist in Your Pocket, Scan Food, Get Answers | 1 | |
|
| 25. | Best Practices for Protecting Data | 1 | |
|
| 26. | Best Practices for Prompt Safety | 3 | |
|
| 27. | Leveraging LLMs for Causal Reasoning | 3 | |
|
| 28. | Open Source Projects for Building Agentic Applications | 5 | |
|
| 29. | How LLMs Can Help RL Agents Learn | 6 | |
|
| 30. | Is Amazon Bedrock Production Ready? | 1 | |
|
| 31. | Scope Management & Balancing Learning Goals When Building Agentic Systems | 0 | |
|
| 32. | Dynamic Prompting and Retrieval Techniques | 0 | |
|
| 33. | Causal Representation Learning | 1 | |
|
| 34. | Agentic Model & Framework Volatility: Risks for Production | 1 | |
|
| 35. | What Makes DeepSeek R1 Multi-token Prediction Unique? | 1 | |
|
| 36. | LLM VLM Based Reward Models | 2 | |
|
| 37. | Use Cases of State Machines | 0 | |
|
| 38. | Limitations of Agentic Frameworks: When to Use a Custom Framework | 0 | |
|
| 39. | LLMs as Agents | 0 | |
|
| 40. | Selecting Tools and Libraries for Agentic Workflows | 1 | |
|
| 41. | Relationship between Reasoning and Causality | 4 | |
|
| 42. | Why AI is Ripe for Healthcare 3 Systemic Pressures | 0 | |
|
| 43. | Why AI Agents Make Sense in Health Care | 1 | |
|
| 44. | Strengths, Challenges, and Problem Formulation in RL | 2 | |
|
| 45. | Building an Agentic App - LangChain Code Demo | 1 | |
|
| 46. | How to Fine Tune Agents | 0 | |
|
| 47. | Examples of Causal Representation in Computer vision | 0 | |
|
| 48. | IdeaProof: An AI That Turns Half-Baked Ideas Into Actionable Strategies | 1 | |
|
| 49. | Questions to Answer before Building Your Next Product | 0 | |
|
| 50. | Building an Agentic App - Challenges of No Code Tools | 3 | |
|
| 51. | How to Create and Customize a Knowledge Base for LLMs in Dify | 3 | |
|
| 52. | Tokenization in DeepSeek R1 | 1 | |
|
| 53. | How Do State Machines Work? | 2 | |
|
| 54. | What are Agents | 0 | |
|
| 55. | How to Create a Knowledge Base on Amazon Bedrock | 2 | |
|
| 56. | Using Open Source Framework Versus Industry Standard Like LangChain | 2 | |
|
| 57. | Multi Agent Architecture: Using AI Agents in Game Development | 3 | |
|
| 58. | Budgeting for MVP Deployment | 0 | |
|
| 59. | XAI for LLMs: looking under the hood of Large Language Models | 21 | |
|
| 60. | Semi Supervised Learning - Session 8 | 0 | |
|
| 61. | MLOps: Packaging Overview, Session 1, part 5 | 0 | |
|
| 62. | Machine Learning for Cyber Security - Session 15 | 0 | |
|
| 63. | Reinforcement Learning: Q&A, Closing - Session 16 | 0 | |
|
| 64. | MLOps: Flask Iris Model Serving, Session 2, part 1 | 0 | |
|
| 65. | Machine Learning for Cyber Security - Session 7 | 0 | |
|
| 66. | Azure MLops- MLPipeline_MNIST Hands-on- Session II, part 5 | 0 | |
|
| 67. | Machine Learning and Optimization - Deep Random Talks - Episode 17 | 0 | |
|
| 68. | AI Product, Part 6: Product Team | 0 | |
|
| 69. | MLOps: Common Serialization Approaches, Session 2, part 1 | 0 | |
|
| 70. | AI Product, Part 9: Long-term Validation | 0 | |
|
| 71. | AI Product, Part 7: Storytelling | 0 | |
|
| 72. | MLOps: Introduction/Overview, Session 1, part 1 | 0 | |
|
| 73. | AI Product, Part 1: Principles | 0 | |
|
| 74. | AI Product, Part 3: Ideation | 0 | |
|
| 75. | Graph Neural Networks, Session 5: Graph Attention Networks | 0 | |
|
| 76. | Graph Neural Networks, Session 4: Simple Graph Convolution | 0 | |
|
| 77. | AI Product, Part 2: Frameworks | 0 | |
|
| 78. | Semi Supervised Learning - Session 7 | 0 | |
|
| 79. | MLOps: MLflow Hands On, Session 2, part 2 | 0 | |
|
| 80. | Semi Supervised Learning - Session 10 | 0 | |
|
| 81. | Graph Neural Networks, Session 6: DeepWalk and Node2Vec | 0 | |
|
| 82. | Machine Learning and Empathy - Deep Random Talks - Episode 16 | 0 | |
|
| 83. | Machine Learning and Future of Education - Deep Random Talks - Episode 14 | 0 | |
|
| 84. | Machine Learning for Cyber Security - Session 4 | 0 | |
|
| 85. | Learn about Foodshake and it’s vegan recipes! | 0 | |
|
| 86. | Mathematics of Deep Learning: Convnets- Session 9 | 0 | |
|
| 87. | Machine Learning on Source Code - GitHub / Open AI Copilot | 0 | |
|
| 88. | Machine Learning for Weather Forecast - Deep Random Talks - Episode 15 | 0 | |
|
| 89. | Graph Neural Networks, Session 2: Graph Definition | 0 | |
|
| 90. | Explainable AI, Session 2: Why Do We Need Machine Learning Explanations | 0 | |
|
| 91. | MLOps: Creating an AWS account and GitHub account, Session 1, part 2 | 0 | |
|
| 92. | AI Product, Part 4: Talking to Users | 0 | |
|
| 93. | Explainable AI, Session 5: Intro to SHAP | 0 | |
|
| 94. | Explainable AI, Session 3: Explainability Options | 0 | |
|
| 95. | AI Product, Part 8: Short-term Validation | 0 | |
|
| 96. | Machine Learning and Fraud Detection - Deep Random Talks - Episode 13 | 0 | |
|
| 97. | Graph Neural Networks, Session 1: Introduction to Graphs | 0 | |
|
| 98. | Azure MLops- Model Deployment- Session III, part 2 | 0 | |
|
| 99. | AI Product, Part 5: Discovery | 0 | |
|
| 100. | Symmetries in Deep Learning - Deep Random Talks - Episode 18 | 0 | |
|
