🚀 Generative AI Training Program Overview

This repository presents a comprehensive and modular Generative AI Training Plan, designed to equip learners with both foundational knowledge and advanced skills in modern AI systems. The multi-week curriculum systematically covers theory, architecture, and practical applications of LLMs, multimodal AI, and autonomous agents.

🧠 Key Components

🔹 AI Ecosystem & Evolution

Understand the progression from traditional AI to Deep Learning, Reinforcement Learning, and Generative AI, with a detailed look at the LLM ecosystem and evaluation methods.

🔹 Prompt Engineering

Learn and apply techniques like zero-shot, few-shot, chain-of-thought, and tree-of-thought prompting to harness LLM capabilities effectively.

🔹 Deep Learning Foundations

Explore core concepts including multilayer perceptrons (MLPs), activation functions, backpropagation, and RNNs.

🔹 Transformers & Attention

In-depth coverage of self-attention, transformer architecture, and encoder-decoder variants powering today’s state-of-the-art models.

🔹 LLM Internals & Fine-Tuning

Dive into LLM architecture, training strategies, and techniques for customizing models to domain-specific tasks.

🔹 Agentic AI

Introduction to autonomous AI agents that perform goal-driven tasks with memory, reasoning, and tool usage capabilities.

🔹 RAG (Retrieval-Augmented Generation)

Learn to integrate LLMs with external knowledge sources for dynamic, context-rich response generation in enterprise applications.

🔹 MCP (Multimodal Co-Pilot)

Explore the future of AI through multimodal systems that combine text, vision, and voice for next-generation user experiences.

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Whether you're a researcher, engineer, or enthusiast, this training plan provides a guided pathway to mastering the theory and practice of Generative AI and its real-world applications.

Course Content

Lecture Name	Topic Covered
Lecture-1	History of AI, Paradigm Shifts in AI, AI vs ML vs DL, Generative AI, Reinforcement Learning and Autonomous Agents
Lecture-2	Evolution of Language Models, Prompt Engineering: Zero shot, Few Shot, Chain of Thought, Chan of Thought, Multi Turn Prompting, Role Play
Lecture-3	Neuron architecture (input, weights, bias, output), Forward propagation in MLP, Multi-layer structure (hidden layers), Weight initialization

Generative AI Training

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