Join Applied Generative AI MASTERY
Build Real-Life industry Focused Gen AI Applications
- Start Date : 03.05.2025
- Duration : 10-12 Sessions
- Mode : Live Classes
- Classes : Every Saturday 7:00 PM IST
What will you Learn ?
- Module 1 : Python Programming Language
- Variables - strings, numbers
- Data Types - List, Dictionary, dataframes
- Decision making - if-else
- Iteration - loops
- Installing and importing packages
- Reading / Writing data
- Functions
- List Comprehension, generators, Decorators
- Classes and Objects
- Module 2 : Introduction to Generative AI
- Frontier LLMs
- Comparative analysis of LLMs
- How to read Leaderboards like Vellum, huggingface, Scale AI
- How to read the benchmarks
- Setting up ollama to install free LLMs (LLama, Mistral) on local computer
- Understand LLMs and experiment with tools like ChatGPT
- Module 3 : Prompt Engineering
- Play with prompts to understand their role
- Give a persona to LLM
- How to train LLMs using prompt engineering
- Zero shot Prompting
- One shot Prompting
- Few shot prompting
- Chain of Thoughts
- Flipped Approach
- Module 4 : Getting Started with Huggingface
- Play with prompts to understand their role
- Huggingface ecosystem - Models, Datasets,Spaces
- Transformer pipeline as a high level helper
- Transfer AutoModelForCausalLM to load model directly
- Vector Embeddings
- Module 5 : Langchain and Langsmith
- Build Application using free and paid LLM APIs
- Agents with Langchain
- Gradio UI
- Understand LLMs and experiment with Langchain
- How to build applications with memory like chat history
- Learn LangChain for scalable LLM workflows.
- How to combine tools with gen ai application
- How to log and track applications using Langsmith
- Module 6 : Retrieval Augmentation Generation (RAG)
- Split big documents into smaller documents using chunking methods like fixed size, semantic chunking
- Learn Embedding models to convert text into vectors
- Learn vector databases (ChromaDB, FAISS)
- Save the vectors in vector stores
- Build LLM applications with domain-specific knowledge using RAG systems.
- Module 7 : LangGraphs - Agentic AI
- Agentic AI
- Cyclic chains
- Flow engineering
- Understand frameworks like Crew AI and AutoGen for building autonomous systems.
- Combine reasoning, planning, and action with LLMs for complex workflows
- Module 8 : Deploying Gen AI applications
- Deployment Strategies
- LLMOps
- Monitoring LLM Inference Endpoints
- LLM Evaluation - BERT Score, ROUGE, Cosine Similarity, Human Evaluation
- How to safeguard LLM Application
- Module 9 : Fine Tuning Model
- LORA
- Q-LORA
- How to retrain specific layers of LLMs
- Weights & Biases - How to analyze and visualize during training
- Customize LLMs for domain-specific tasks
- Use curated datasets and optimized hyperparameters for precision.
- Module 10 : Multimodal Generative AI
- Variational Autoencoders (VAEs), Generative Adversarial Models(GANs), Autoregressive models
- Diffusion models - Unconditioned and Conditioned models
- Stable Diffusion
- Generative AI for Images
- Generative AI for Audio
- Generative AI for Videos
- Generative AI Real World Projects
1. | Source Code Analyzer |
2. | Code Converter – Python code to optimized C++ code |
3. | Youtube Video Content Summarizer |
4. | ChatBot for Medical Industry |
5. | Document chatbot having memory/history using RAG |
6. | Administrative AI Assistant to create meetings minutes |
7. | Personalized Math Problem Solver like Mathos AI and MathGPT |
8. | Audio Project – Create Music from text |
9. | Video – Create video from user provided text |
10. | Multimodal applications – audio, text, image |
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