This section starts with the broad idea of AI, then moves through machine learning, deep learning, and ways to adapt trained models. The goal is to explain what a model learns from data and how the major approaches differ before dealing with mathematics or implementation.
Follow the three-stage learning path
Start with What Is Machine Learning?. It contrasts rules written by people with models learned from data, then separates supervised, unsupervised, and reinforcement learning by the feedback available during training.
Continue with What Is Deep Learning?. It shows how multilayer neural networks learn useful representations from complex data and explains when a simpler machine-learning method may still be the better choice.
Finish with Ways to Adapt and Share Model Learning. It distinguishes transfer learning, fine-tuning, multitask learning, and federated learning by asking what is shared, what is updated, and where the data remains.
Choose a starting point
If AI and machine learning still feel interchangeable, begin with the first page and read in order. If you already understand neural networks at a high level, start with deep learning. If your immediate question is how to adapt an existing model to new work, go directly to the final page.
No special mathematical background is required for the conceptual path. Functions, probability, and vectors become useful when you later inspect the calculations inside a model.