Advanced Course
Advanced
Generative AI
and NLP
This course provides an in-depth exploration of Generative AI and NLP through practical projects.
5
14 enrolled students
Course Overview
This course provides an in-depth exploration of Generative AI and NLP through practical projects. You’ll learn about foundational concepts, tools, and methods for developing, fine-tuning, and deploying large language models (LLMs). By the end of the course, you’ll be able to apply advanced techniques like prompt engineering, fine-tuning, and multi-agent systems to build real-world AI applications.
Basic To Advance
You will progress through this course from basics to advanced level.
Duration
3 Months
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Modules
Module 1: Introduction to Generative AI and AI Models
Objective:
- Understand generative AI concepts and gain familiarity with popular LLMs.
- Explore model interfaces like Bing Copilot and understand different chat platforms.
Topics:
- Understanding Generative AI through Bing Copilot
- Introduction to Chat Interfaces
- Overview of ChatGPT (OpenAI), Claude (Anthropic), Meta LLaMA (WhatsApp, Facebook, Instagram), and Mistral (Le Chat)
Hands-on Exercise:
- Interacting with APIs: Set up API connections and experiment with different models (e.g., OpenAI’s API, Anthropic’s Claude, Meta’s LLaMA).
- Response Comparison: Analyze model responses, comparing output quality across models and documenting key differences
Module 2: Prompt Engineering
Objectives:
- Learn prompt creation and best practices for effective prompt engineering across various LLMs.
- Optimize prompts for specific model tasks.
Topics :
- 1 Introduction to Prompt Engineering
- Overview and best practices for prompt creation (using the Prompt Engineering Guide).
- 2 Advanced Prompt Engineering Techniques
- Techniques for optimizing prompts for various LLMs (using the Advanced Prompting Guide).
Hands-on Exercise:
- Prompt Experimentation: Create scripts to test various prompts and analyze LLM responses.
- Prompt Optimization: Adjust prompts iteratively to improve relevance, coherence, and Conciseness.
Module 3: LangChain for Generative AI Applications
Objectives:
- Understand LangChain and LangHub for prompt management and API integration.
- Build interactive applications with LangChain in Python.
Topics Covered:
- 1 Introduction to LangChain and LangHub
- Using LangChain for prompt management and API integration.
Hands-on Exercise:
- Basic LangChain Implementation: Build a prompt-driven application using LangChain.
- API Management: Manage multiple APIs in an application using LangChain.
Project 1: Prompt + LangChain
Create an interactive prompt-driven application using LangChain to explore prompt-response workflows and API handling.
Module 4: Generative AI Patterns and Applications
Objectives:
- Learn common generative AI patterns like RAG, function calling, and multi-agent systems.
- Implement vector databases and RAG for efficient data retrieval.
Topics:
- 4.1 Overview of Generative AI Patterns
- Retrieval-Augmented Generation (RAG), Function Calling, Agents, Tools, and Multi-Agent Systems.
- 4.2 Vector Databases for LLMs
- Introduction to vector databases and their integration with AI applications.
Hands-on Exercise:
- RAG Implementation in LangChain: Build a retrieval-augmented generation application.
- Vector Database Integration: Implement a vector database for data retrieval.
Project 2: RAG + LangChain + Prompt
Develop a chatbot or QA system using RAG and LangChain, enhanced by optimized prompts.
Module 5: Fine-Tuning and Optimization of LLMs
Objectives:
- Understand Hugging Face’s tools for NLP, LLMs, and fine-tuning techniques.
- Implement fine-tuning with techniques like PEFT, quantization, and SFT on open-source models.
Topics:
- 5.1 Introduction to Hugging Face Platform
Overview of tools and resources on Hugging Face for NLP and LLMs.
- 5.2 Advanced NLP
Foundations of generative models and LLMs (encoder-decoder architecture, attention mechanisms, etc.).
- 5.3 Fine-Tuning Open-Source LLMs
Techniques: Parameter-Efficient Fine-Tuning (PEFT), Quantization, TRL, PPO, DPO, and SFT.
Models: LLaMA, Mistral.
Hands-on Exercise:
- Model Loading and Fine-Tuning: Load models and fine-tune on a custom dataset using Hugging Face tools.
- Experiment with PEFT and SFT: Apply fine-tuning techniques to optimize LLM performance.
Project 3: Fine-Tuning LLaMA/Mistral for Question Answering
Build a question-answering system by fine-tuning an open-source LLM.
Module 6: Testing and Evaluating LLMs
Objectives:
- Learn metrics like ROUGE-L, MMR, and Perplexity to evaluate LLMs effectively.
- Use evaluation metrics to guide decision-making for model improvements.
Topics:
- 6.1 Evaluation Metrics and Tools
- Introduction to metrics: ROUGE-L, ROUGE-L Sum, MMR, Perplexity, AlignScore, and SummaC.
Hands-on Exercise:
Custom Metrics Evaluation: Use Python libraries to evaluate a model based on ROUGE-L, Perplexity, and other metrics.
Model Comparison: Compare metrics across models and assess fine-tuning results.
Module 7: Model Deployment and Inference
Objectives:
- Gain expertise in deploying LLMs using tools like VLLM, AWQ, GGUF, and LoRa.
- Understand inference setup for LLMs in various environments.
Topics:
- 7.1 Inferring and Deploying LLMs
Tools and frameworks: VLLM, AWQ, GGUF, LoRa, MultiLoRa.
Hands-on Exercise:
Deployment Setup: Write deployment scripts for cloud or local environments using frameworks such as VLLM.
Project 4: Deploy a Question Answering System
Deploy the question-answering system on a cloud-based or local environment.
Module 8: Best Practices, Security, Grounding, and Ethics in AI
Learning Objectives:
- Learn best practices, grounding, and security considerations in AI.
- Understand ethical considerations and responsible model deployment.
Topics:
- 8.0 Best Practices
- 8.1 Security Considerations in AI Applications
- 8.2 Ethical Implications of Generative AI
Module 9: Building an Interactive Application
Learning Objectives:
Gain proficiency in building interactive front-end applications with Streamlit for LLMs.
Topics:
- 9.1 Streamlit for AI Applications
Hands-On Exercises:
- Streamlit Integration: Build a basic Streamlit app with a question-answering model.
Module 10: Final Project - Interactive Question Answering System with SQL/Graph DB
Learning Objectives:
- Apply the skills learned to create a full-stack, interactive question-answering system.
Final Project:
Create and deploy a Streamlit app integrating a question-answering LLM with SQL or Graph DB for data storage and retrieval.
Learning Outcomes:
By completing this course, participants will be able to:
- Develop and fine-tune LLMs for specific NLP tasks.
- Use LangChain and prompt engineering techniques to build advanced AI systems.
- Apply industry best practices in model evaluation, deployment, and ethical AI usage.
Frequently Asked Questions
1. What is the Advanced Generative AI and NLP Applications course about?
This course delves into advanced techniques and real-world applications of generative AI and natural language processing (NLP). You will learn to build and fine-tune sophisticated models for text generation, summarization, sentiment analysis, translation, chatbots, and other language-based AI applications. The course emphasizes hands-on experience with cutting-edge models, such as GPT, BERT, and Transformer architectures.
2. What kind of projects will I work on?
You will work on real-world projects like building a chatbot using a transformer model, implementing a sentiment analysis tool, fine-tuning a text summarization model, and creating a custom language model for specific domains. These projects are designed to give you experience with advanced NLP tasks.
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