What is a Neural Network?
A neural network is a type of computer program loosely inspired by how the human brain works. Just as your brain uses billions of connected neurons to think, neural networks use artificial "neurons" (simple math functions) connected together to process information.
Despite the name, neural networks don't actually work like biological brains. They're mathematical models that are very good at finding patterns in data.
Key Insight
A neural network learns by adjusting thousands or millions of numbers (called "weights") until it gets good at a specific task, like recognizing cats in photos.
How Neural Networks Are Structured
Neural networks are organized in layers:
- Input layer — Receives the data (like pixels of an image)
- Hidden layers — Process the data through multiple transformations
- Output layer — Produces the final result (like "cat" or "dog")
Each "neuron" in a layer takes inputs, multiplies them by weights, adds them up, and passes the result through a function. This simple operation, repeated millions of times across many neurons, can recognize faces, translate languages, or play games.
How Neural Networks Learn
Training a neural network involves these steps:
- Forward pass — Feed data through the network and get a prediction
- Calculate error — Compare the prediction to the correct answer
- Backpropagation — Figure out which weights caused the error
- Update weights — Adjust weights to reduce the error
- Repeat — Do this millions of times with different examples
Types of Neural Networks
Feedforward Networks
The simplest type. Data flows in one direction, from input to output. Good for basic classification tasks.
Convolutional Neural Networks (CNNs)
Specialized for images. They use "filters" that slide across images to detect features like edges, shapes, and textures. Used in facial recognition, medical imaging, and self-driving cars.
Recurrent Neural Networks (RNNs)
Designed for sequences like text or time series. They have "memory" that lets them consider previous inputs. Used in language translation and speech recognition.
Transformers
The architecture behind ChatGPT and modern AI. They can process entire sequences at once and understand relationships between distant words. Revolutionized AI in 2017.
Why Neural Networks Work So Well
- Pattern recognition — They excel at finding patterns too complex for humans to program manually
- Generalization — They can apply learned patterns to new, unseen data
- Scalability — Bigger networks with more data generally perform better
Limitations
- Black box — It's often unclear why they make specific decisions
- Data hungry — They need massive amounts of training data
- Computationally expensive — Training large networks requires specialized hardware
- Brittleness — Small changes to input can cause unexpected errors
Summary
- • Neural networks are math models inspired (loosely) by the brain
- • They learn by adjusting weights to minimize errors
- • Different architectures suit different tasks (CNNs for images, Transformers for text)
- • They're powerful but require lots of data and computing power