I share my experience with overfitting in machine learning. I was fooled by 99% training accuracy, only to fail in production. Learn how I used Dropout, Regularization, and Data Augmentation to build 'real intelligence' instead of a memorization machine.
ChatGPT answers questions. AI Agents plan, use tools, and complete tasks autonomously. Understanding this difference changes how you build with AI.
I actually used all three AI coding tools for real projects. Here's an honest comparison of Copilot, Claude Code, and Cursor.
My AI chatbot was hallucinating wild answers to customers. Here's how I implemented RAG (Retrieval-Augmented Generation) to fix it, covering Vector DBs, Embeddings, and Hybrid Search.
Overcoming RNN's 'dementia' and how Google flipped the world with 'Attention Is All You Need'. From Query-Key-Value library analogies to Multi-Head Attention and Vision Transformers.
Now, when I see 99% training accuracy, I get suspicious instead of excited. "You memorized it again, didn't you? Where are you trying to scam me?"
What matters is Validation Accuracy and Test Accuracy. A good model has a small gap between Train and Test scores.
Remember, our goal is not to build a 'Hard Drive (Memorization)' but 'Intelligence (Generalization)'. The sandwich incident was painful, but thanks to it, I learned the most important lesson in Data Science. "Trust the data, but never trust the model's memory."
