Generative AI Bootcamp

Mastering Generative AI: A Comprehensive Guide

Get Started with the AI Ecosystem:

• Anaconda Setup: Learn to download and install the powerful Anaconda Distribution, the essential platform for data science and AI development.
• Integrated Development Environment: Install and configure Jupyter Notebook and Visual Studio Code for a streamlined coding experience.
• GPU Acceleration: Unlock the power of your GPU! We'll guide you through installing the CUDA Toolkit, cuDNN, and PyTorch to enable GPU acceleration for faster model training and inference.

Python Fundamentals for AI:

• Python Basics: Build a solid foundation in Python with a comprehensive introduction covering variables, data types, operators, control flow (if/else, loops), functions, and string manipulation.
• Package Management: Master package installation and import techniques to leverage the vast Python ecosystem.
• Data Structures: Understand and utilize fundamental data structures like lists, tuples, sets, and dictionaries for efficient data handling.

Understanding the Core Concepts of AI:

• AI, Machine Learning, Deep Learning, and Generative AI: Demystify the landscape of Artificial Intelligence. We'll explore the history of AI and differentiate between key concepts like Machine Learning, Deep Learning, and the rapidly evolving field of Generative AI.
• Transformers & Attention Mechanisms: Dive into the inner workings of Transformer models, focusing on the crucial attention mechanism that powers modern AI.

Foundation Models: The Building Blocks of Generative AI

• Foundation Model Deep Dive: Explore the world of Foundation Models – what they are, their history, diverse applications, different types, and real-world examples.
• Language Model Performance: Gain insights into evaluating and comparing the performance of different language models.
• Top Open-Source LLMs: Discover and compare leading open-source Large Language Models (LLMs).
• Choosing the Right Foundation Model: Learn the factors to consider when selecting the optimal Foundation Model for your specific needs.

Responsible AI & Bias Mitigation:

• Responsible AI Practices: Understand the ethical considerations surrounding AI development and the importance of addressing biases in your models.

Building Generative AI Applications with LangChain & RAG:

• Generative AI Application Development: Learn practical techniques for building powerful Generative AI applications using LangChain and Retrieval-Augmented Generation (RAG).
• Retrieval-Augmented Generation (RAG): A deep dive into RAG, covering input preparation, chunking methods, embeddings, vector stores, similarity search, and the complete RAG pipeline.
• Vector Databases for RAG: Explore popular vector databases like Pinecone, Chroma, Weaviate, Milvus, and FAISS, and understand their role in RAG systems.
• Text Chunking Methods: Master various text chunking methods, including Character Text Splitter, Recursive Character Text Splitter, Markdown Header Text Splitter, and Token Text Splitter, to optimize RAG performance.
• Prompt Engineering: Unlock the power of prompt engineering! Learn how to craft effective prompts to guide LLMs towards desired outputs.
  • Prompting Techniques: Explore various prompting techniques: Basic Prompting, Role-Task-Context Prompting, Few-Shot Prompting, Chain-of-Thought Prompting, and Constrained Output Prompting.

LangChain Components & Chains:

• Document Loaders: Learn to load data from various sources using Document Loaders like CSVLoader, HTMLLoader, and PDFLoaders.
• Memory for LLMs: Understand how to provide memory to Large Language Models (LLMs) to enable conversational capabilities.
  • Memory Types: Explore different memory types, including ConversationBufferMemory, Conversation Buffer Window, and ConversationSummaryMemory.
• Chaining LangChain Components: Learn how to combine different LangChain components to create complex workflows.
• LangChain Chains: Master various LangChain chains, including Single Chain, Simple Sequential Chain, Sequential Chain, Math Chain, RAG Chain, Router Chain, LLM Router Chain, and SQL Chain.

Multi-Agent Frameworks with CrewAI:

• Multi-Agent Systems: Build sophisticated multi-agent systems using CrewAI and LangChain tools.

Hugging Face & Model Utilization:

• Hugging Face Ecosystem: Explore the Hugging Face platform and learn how to utilize models from the Hugging Face Hub to build Generative AI applications.