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✅ Roadmap To Learn Gen AI: Step-by-Step Guide

1• Grasp the Basics of AI
◦ Understand AI, ML, DL differences
◦ Learn types of AI: narrow, general, super
◦ Explore real-world AI applications

2• Learn Python for AI
◦ Master Python fundamentals
◦ Use libraries: NumPy, Pandas, Matplotlib
◦ Learn basic data preprocessing

3• Master Machine Learning Concepts
◦ Supervised vs. Unsupervised learning
◦ Regression, classification, clustering
◦ Overfitting, underfitting, bias-variance tradeoff

4• Dive Into Deep Learning
◦ Neural networks: forward & backpropagation
◦ Activation functions, loss functions
◦ Use TensorFlow or PyTorch

5• Understand Transformers
◦ Learn about self-attention mechanisms
◦ Understand encoder, decoder, positional encoding
◦ Study “Attention is All You Need” paper

6• Explore Language Modeling
◦ Learn tokenization & embeddings
◦ Understand masked vs. causal language models
◦ Study next-token prediction

7• Get Started with Models
◦ Learn how -2, -3, -4 work
◦ Explore OpenAI Playground
◦ Experiment with Chat

8• Learn About BERT and Encoder-Based Models
◦ Understand masked language modeling
◦ Use BERT for classification, QA tasks
◦ Explore Hugging Face Transformers

9• Dive Into Generative Models
◦ Study GANs, VAEs, Diffusion Models
◦ Understand use cases: image, audio, video

10• Practice Prompt Engineering
◦ Use zero-shot, few-shot, chain-of-thought prompting
◦ Learn how prompt structure affects output
◦ Experiment with different prompt styles

11• Build With OpenAI & Hugging Face
◦ Use OpenAI API (Chat, DALL·E, Whisper)
◦ Learn about Hugging Face Spaces & Models
◦ Deploy simple GenAI apps

12• Work With LangChain
◦ Build AI pipelines with LangChain
◦ Use agents, memory, tools
◦ Connect LLMs with external data sources

13• Create Real-World GenAI Projects
◦ Build AI content writers, chatbots
◦ Try text-to-image, text-to-code apps
◦ Use pre-built APIs to accelerate development

14• Learn RAG (Retrieval-Augmented Generation)
◦ Understand how LLMs retrieve/generate answers
◦ Use tools like LlamaIndex, Haystack
◦ Connect with vector databases (e.g., Pinecone)

15• Experiment with Fine-Tuning
◦ Learn difference between fine-tuning and prompt engineering
◦ Try LoRA, PEFT for efficient training
◦ Use domain-specific datasets

16• Explore Multi-Modal GenAI
◦ Work with tools like -4V, ChatGPT, LLaVA
◦ Learn image-to-text, text-to-image models
◦ Understand use cases in design, vision, more

17• Study Ethics & AI Safety
◦ Understand AI bias, explainability
◦ Explore safety practices & fairness
◦ Learn about responsible AI deployment

18• Build AI Agents & Workflows
◦ Use tools like Auto-, CrewAI, OpenAgents
◦ Create workflows for automation
◦ Deploy agents for real-world tasks

19• Join AI Communities
◦ Engage on Hugging Face, Discord, Reddit, Twitter
◦ Follow top AI researchers
◦ Contribute to open-source tools

20• Stay Updated & Keep Experimenting
◦ Read research papers, attend conferences
◦ Keep testing new APIs, models, frameworks
◦ Continuously build & share your work

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