The Deep Learning Revolution
Neural Networks
Meaning: Models inspired by the brain, with "neurons" (math units) connected in layers.
Example: Show it thousands of cat photos → it learns patterns like "pointy ears + whiskers = cat."
Deep Learning
Meaning: Neural networks with many layers (hence "deep") that can extract complex features automatically.
CNN (Convolutional Neural Network)
Meaning: A deep learning model made for images.
Detailed Example - Self-driving cars:
- CNN layer 1: Detects edges (road lines).
- CNN layer 2: Detects shapes (cars, people, traffic lights).
- CNN layer 3: Makes decision → "STOP, pedestrian ahead."
RNN / LSTM (Recurrent Neural Networks)
Meaning: Models made for sequences like speech or text.
Detailed Example - Old Siri/Alexa:
- Input = "What's the weather?"
- RNN remembers the sequence of sounds → turns them into text → fetches weather.
Breakthrough Moment (2012)
- ImageNet competition → Deep CNNs crushed traditional ML by a huge margin.
- Suddenly, AI could see and hear much better.
Healthcare Example:
- CNN trained on millions of X-rays.
- Learns what tumors look like.
- Detects cancer as accurately as a radiologist (sometimes faster).
Limitation: Needs millions of data samples and expensive GPUs.
The Car Analogy
Deep Learning = A car that learns tuning itself after driving thousands of miles.
Unlike classical ML where humans picked features, deep learning automatically discovers what features matter by processing massive amounts of raw data.