This comprehensive learning plan will guide you through mastering the most important tools and techniques in the generative AI ecosystem, with practical projects and resources at every step.
Foundations
Objective
Build a solid foundation in machine learning and deep learning concepts
Learn Python for ML
Topics: Data manipulation, libraries (NumPy, pandas), plotting (matplotlib)
Resources: Python for Data Science Handbook, FreeCodeCamp's Python courses on YouTube
Machine Learning Basics
Topics: Regression, classification, overfitting, evaluation metrics
Resources: Andrew Ng's Machine Learning, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow"
Introduction to Deep Learning
Topics: Neural networks, activation functions, backpropagation
Resources: Deep Learning Specialization by Andrew Ng, Neural Networks from Scratch
Master PyTorch
Objective
Gain proficiency in PyTorch for building and training neural networks
Beginner's Guide to PyTorch
Topics: Tensors, autograd, nn.Module, datasets, and dataloaders
Resources: PyTorch Official Tutorials
Build Custom Neural Networks
Tasks: Image classification (MNIST/CIFAR-10 datasets)
Resources: "Deep Learning with PyTorch" by Eli Stevens
Pre-trained Models
Topics: Transfer learning, fine-tuning
Resources: Transfer Learning Tutorial
Hugging Face
Objective
Understand transformer models and the Hugging Face ecosystem
Hugging Face Basics
Topics: Transformers library, tokenizers, and datasets
Resources: Hugging Face Course
Working with Pre-trained Models
Tasks: Text classification, sentiment analysis, summarization
Resources: Hugging Face Transformers Documentation
Fine-Tuning with Hugging Face
Topics: Fine-tuning BERT, GPT, T5 models
Resources: Fine-Tuning Transformers
LangChain
Objective
Learn how to use LangChain to build LLM-powered applications
Introduction to LangChain
Topics: Chains, memory, agents, tools
Resources: LangChain Documentation
Building Applications
Tasks: Question-answering systems, document-based chatbots
Advanced Features
Topics: Custom tools, chaining multiple tasks, external API integration
RAG
Objective
Learn how Retrieval-Augmented Generation combines retrieval with generative models
RAG Basics
Topics: Concept, retrievers, generators, vector stores
Resources: RAG with Hugging Face
Implementing RAG Pipelines
Tasks: Document search + generation systems
Tools: LangChain + Hugging Face
Resources: LangChain RAG tutorials, Hugging Face RAG Examples
Fine-Tuning
Objective
Master the process of fine-tuning generative models for specific tasks
Fine-Tuning Concepts
Topics: Transfer learning, hyperparameter tuning
Resources: Hugging Face Fine-Tuning Guides, Google Colab for experiments
Fine-Tune Models
Tasks: Fine-tune GPT, T5, or custom transformers for NLP tasks
Resources: Hugging Face Trainer API or PyTorch Lightning
Evaluate Fine-Tuned Models
Topics: BLEU, ROUGE, perplexity, and real-world evaluation
Generative AI Applications
Objective
Build end-to-end generative AI solutions
Building Chatbots
Tools: LangChain, RAG, fine-tuned LLMs
Task: Real-time Q&A over documents
Creative Generation
Tasks: Text, image, and code generation applications
Tools: Hugging Face, OpenAI, Stability AI
Deploy Models
Tools: FastAPI, Streamlit, or Docker for deploying applications
Resources: Hugging Face Spaces, Deployment tutorials on AWS or Google Cloud
Practical Projects
Objective
Reinforce knowledge by working on real-world projects
Ideas for Projects
• News summarizer using Hugging Face
• Custom RAG-powered knowledge assistant
• Multi-modal app combining text and image generation
Collaboration
• Participate in open-source projects on GitHub
• Share projects on Kaggle or Hugging Face Spaces
Stay Updated
Objective
Keep up with advancements in generative AI
Blogs and Newsletters
• OpenAI Newsletter
Participate in Communities
• Hugging Face forums, Reddit, and Discord groups