Full Series [Part 1-18] | Generative AI for Beginners

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Learn the fundamentals of building Generative AI applications with our 18-lesson comprehensive course by Microsoft Cloud Advocates.

Chapters:
00:00:00 - Introduction to Generative AI and LLMs [Pt 1]
00:10:36 - Exploring and comparing different LLMs [Pt 2]
00:31:34 - Using Generative AI Responsibly [Pt 3]
00:40:54 - Understanding Prompt Engineering Fundamentals [Pt 4]
01:04:08 - Creating Advanced Prompts [Pt 5]
01:21:06 - Building Text Generation Applications [Pt 6]
01:36:37 - Building Chat Applications [Pt 7]
01:50:10 - Building Search Apps Vector Databases [Pt 8]
02:08:08 - Building Image Generation Applications [Pt 9]
02:30:32 - Building Low Code AI Applications [Pt 10]
02:46:55 - Integrating External Applications with Function Calling [Pt 11]
02:56:06 - Designing UX for AI Applications [Pt 12]
03:08:38 - Securing Your Generative AI Applications [Pt 13]
03:16:49 - The Generative AI Application Lifecycle [Pt 14]
03:29:12 - Retrieval Augmented Generation (RAG) and Vector Databases [Pt 15]
03:40:03: - Open Source Models and Hugging Face [Pt 16]
03:51:32 - AI Agents [Pt 17]
03:59:26 - Fine-Tuning LLMs [Pt 18]

[QuantumXdeveloper]

Chapters:
00:00:00 - Introduction to Generative AI and LLMs [Pt 1]
00:10:36 - Exploring and comparing different LLMs [Pt 2]
00:31:34 - Using Generative AI Responsibly [Pt 3]
00:40:54 - Understanding Prompt Engineering Fundamentals [Pt 4]
01:04:08 - Creating Advanced Prompts [Pt 5]
01:21:06 - Building Text Generation Applications [Pt 6]
01:36:37 - Building Chat Applications [Pt 7]
01:50:10 - Building Search Apps Vector Databases [Pt 8]
02:08:08 - Building Image Generation Applications [Pt 9]
02:30:32 - Building Low Code AI Applications [Pt 10]
02:46:55 - Integrating External Applications with Function Calling [Pt 11]
02:56:06 - Designing UX for AI Applications [Pt 12]
03:08:38 - Securing Your Generative AI Applications [Pt 13]
03:16:49 - The Generative AI Application Lifecycle [Pt 14]
03:29:12 - Retrieval Augmented Generation (RAG) and Vector Databases [Pt 15]
03:40:03 - Open Source Models and Hugging Face [Pt 16]
03:51:32 - AI Agents [Pt 17]
03:59:26 - Fine-Tuning LLMs [Pt 18]

[gregorysun]

Thanks a lot for this session. It was very useful and provided a comprehensive overview of Generative AI. help me to have a solid understanding of the basic concepts and the overall landscape of this exciting field.

[Gaurav-pqug]

00:11 Introduction to generative AI and large language models
02:51 Generative AI powered by Transformer architecture for large language models
08:36 Different types of prompts in generative AI for educational scenarios
11:15 Comparison: Foundation Models vs. LLMs and Language Models
16:01 Comparison between open source and proprietary AI models
18:09 Generative AI allows easy image and text generation
22:11 Exploring and testing large language models for AI applications
24:24 Model deployment options and model benchmarking in H Studio
29:04 Fine-tuning customizes LLM for specific tasks
31:15 Prioritize responsible AI in building generative AI applications
35:16 Using generative AI responsibly, including safety measures and diverse prompts.
37:20 Implement specialized models and safety systems for better AI usage.
41:19 Prompt engineering involves iterating on the text input to achieve desired model responses.
43:07 Prompt engineering optimizes model responses
46:46 Exploring text generation with different parameters and models
48:36 Understanding the power and challenges of prompt engineering.
52:28 Prompt engineering model has issues with stochastic responses and fabrication.
54:24 Prompt engineering enhances AI conversation responses
58:12 Generative AI can provide primary content and cues for better response accuracy.
1:00:08 Demonstrating zero-shot prompting and few-shot prompting for generating responses.
1:03:45 Learn how to apply prompt engineering techniques
1:05:46 Drill down into specifics for better understanding
1:09:38 Using Chain of Thought to improve the accuracy of AI responses
1:11:39 Teaching AI through Chain of Thought
1:15:38 Context-based response generation
1:17:45 Context matters in data science prompting
1:21:37 Setting up libraries and environment variables for AI development
1:23:46 Importance of specifying the right API version and deployment
1:27:48 Generative AI can generate unique stories based on prompts.
1:29:43 Creating a user-driven recipe generation app
1:33:43 Learned how to improve AI prompt and build different apps using AI.
1:35:47 Generative AI can create contextually relevant responses in real time.
1:40:26 Fine-tuning is important for specialized domains
1:42:35 Azure services architecture for AI search and open AI
1:47:06 Demonstration of using Azure open AI endpoints and chat app
1:49:33 Semantic search and the use of vectors in enabling effective search
1:53:31 Introduction to text embedding models
1:55:31 Visualizing vectors in two and three-dimensional space.
1:59:18 Using pandas data frame for prototyping
2:01:09 Utilizing chunking and overlap for better context in text analysis.
2:05:05 Using cosine similarity and embedding to find similar videos
2:07:10 Introducing studio notebooks and resources for learning about AI and search applications.
2:11:32 Generative AI can create images from text descriptions using embeddings and neural networks
2:13:43 Generative AI can be used for prototyping and image generation.
2:17:53 Property manager in London seeking AI image generation for visualizing properties.
2:19:48 Changing settings to modify image style and quality
2:23:42 Adjusting image properties for a natural look
2:25:44 Optimizing prompts for better AI output
2:29:54 Using generative AI in Power Platform for building no code AI applications
2:32:05 AI integration in daily productivity tools
2:36:28 Using generative AI in Power Automate for quick automation building
2:38:33 Integrating AI capabilities within Power Platform
2:42:41 Generative AI empowers users to create custom prompts optimized for business scenarios
2:44:47 Key steps for building an effective prompt for Generative AI
2:48:49 Importance of precise formatting in model responses
2:50:36 Importance of ensuring data format consistency for successful function calling.
2:54:02 Model interprets user as a beginner student interested in learning Azure
2:55:55 Designing user experience for generative AI applications
3:00:34 Calibrating trust in generative AI
3:02:44 Understanding neural networks and user interaction in AI applications.
3:07:09 Designing user experience for AI applications requires more than just functionality.
3:09:20 Examining threats, methods, and security testing for generative AI applications
3:13:19 Security and trust in generative AI usage
3:15:13 Ensure using latest secure versions and verifying plugins and model for correctness.
3:19:31 Introduction to LLm Ops and AI metrics
3:21:38 Generative AI enables app developers to participate in AI platforms
3:25:58 Understanding Evaluation Process for RAG based LLN
3:28:18 Retrieval argumented generation and Vector data builds
3:32:59 Interaction with large language model
3:35:17 Creating and ranking a search index for related data retrieval
3:39:34 Understanding open source models in AI development
3:41:37 Olmo models from Allen AI are notable in the realm of open source models
3:45:39 Diverse fine-tune models available on Hugging Face Hub
3:47:31 Understanding Llama 370b and MROL 22b models for enhanced application performance
3:51:24 AI agents and their role in generative AI applications
3:53:27 Exploring tools for managing state and interacting with large language models.
3:57:25 Code-first agent framework uses GPT 3.5 turbo and manages state for executing tasks.
3:59:19 Fine-tuning improves model performance on specific tasks
4:03:20 Considerations for fine-tuning a model
4:05:05 Understanding the fine-tuning process in Generative AI
4:08:33 Fine tuning steps and data preparation
4:10:15 Uploading and validating data set for fine-tuning job
4:13:56 After fine-tuning, evaluate the model
4:15:46 Fine-tuning AI models for customized responses
4:19:21 Train, evaluate, deploy, and utilize the model effectively.

[DanielBellon]

Can't you get the AI to fix the sound quality on your videos MICROSOFT?

[hantuedan]

God Bless Microsoft, thankyou so much

[drew]

Thanks YouTube algorithm, how did you know I was thinking about learning this.

[DanielBellon]

The sound quality is not good for this video it difficult for 4 and 20 minutes with bad sound quality 😢

[sarun]

This is an excellent clip, thank you very much.

[toplssstang]

where can i grab the git for the chat app @1:49:00?

[webdevpersion]

I am from Afghanistan I am not able to use Gemini API region issue please solve this problem or give a solution

[HEYCutie-nq]

hi mam I am a Indian Telugu person; thanks for your class

[vishalturi]

This course is not useful! This is no point for beginners, Microsoft Azure OpenAI is not open, we have to request access and there are so many restrictions to get access like "use company email, have a subscription from the company, etc." So I think this is not for the beginners many people here from college, students or freshers.

[tinnavatthiptong]

"I would like you to translate this into Thai."

[AbhishekVaid]

This video is too colourful to discuss AI and LLM SOTA.

[zeeshanzia]

Course intro is off putting. Sound quality is not good and intro s bizzare. Instead of developing interest this series is making alergic reactions.