Deep Learning
A specialized subset of machine learning that uses artificial neural networks with multiple layers (hence 'deep') to learn complex patterns in data. Deep learning excels at tasks like image recognition, speech processing, and natural language understanding.
Why It Matters
Deep learning breakthroughs have driven most recent AI advances including ChatGPT, self-driving cars, and medical imaging analysis.
Example
Google Photos automatically recognizing and grouping photos of the same person across thousands of images.
Think of it like...
If machine learning is learning to cook by tasting dishes, deep learning is like developing an incredibly refined palate that can identify individual spices, cooking techniques, and regional influences in a single bite.
Related Terms
Neural Network
A computing system inspired by the biological neural networks in the human brain. It consists of interconnected nodes (neurons) organized in layers that process information and learn to recognize patterns.
Convolutional Neural Network
A type of neural network specifically designed for processing grid-like data such as images. CNNs use convolutional layers that apply filters to detect patterns like edges, textures, and shapes at different scales.
Recurrent Neural Network
A type of neural network designed for sequential data where the output at each step depends on previous steps. RNNs have a form of memory that allows them to process sequences like text, time series, and audio.
Transformer
A neural network architecture introduced in 2017 that uses self-attention mechanisms to process sequential data in parallel rather than sequentially. Transformers are the foundation of modern LLMs like GPT, Claude, and Gemini.