🧠 AI Mastery Journey (2025–2027)

Hi! I'm documenting my daily progress as I master AI, ML, Deep Learning, and build real-world projects.

This repository is my public learning journal, where I post:

• ✅ Daily/weekly logs
• 📊 Code & notebooks for each major project
• 🛠️ Tools I learn
• 📚 Notes from courses, books, and experiments

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📅 Start Date

2025-07-01

🎯 Goal

To master Artificial Intelligence and Deep Learning deeply enough to build, deploy, and monetize real-world AI applications — and ultimately launch my own AI-powered startup.

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🗂️ Project Structure

/logs/                  ← Daily/Weekly learning logs (in Markdown)
/ml-projects/           ← Supervised/Unsupervised ML projects
/deep-learning/         ← CNNs, RNNs, transformers, etc.
/deployment/            ← FastAPI, Docker, cloud deployment examples
/resources/             ← Cheat sheets, book notes, curated lists

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🧾 Sample Log Format

Each log is stored under /logs/YYYY-MM-DD.md

# 2025-07-01

## ✅ What I Did
- Watched CNN lesson from DeepLearning.AI
- Trained MNIST CNN model (97.2% accuracy)

## 💡 What I Learned
- How padding and stride affect feature maps
- Role of dropout in preventing overfitting

## 🧠 Concepts To Revisit
- Backpropagation through convolution layers

## ⏭️ What's Next
- Visualize CNN filters using matplotlib
- Try using batch normalization

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🛠️ Tech Stack I'm Using

• Python, Jupyter Notebooks
• PyTorch, scikit-learn, TensorFlow
• FastAPI, Docker, GitHub Actions
• HuggingFace Transformers, LangChain
• Google Colab, Render, ChromaDB

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📚 Learning Resources

• Deep Learning Specialization – Andrew Ng (Coursera)
• fast.ai Course
• The Deep Learning Book
• ML Crash Course – Google
• Full Stack Deep Learning

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📌 Motivation

“Discipline is remembering what you want.”
― David Campbell

Join me on this journey — and let’s build something incredible.

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