Neural networks are computing systems inspired by the biological neural networks that constitute animal brains. They are a subset of machine learning and artificial intelligence.
Key Characteristics / Core Concepts
- Artificial Neurons: These are the fundamental processing units, mimicking biological neurons. They receive input, perform calculations, and produce output.
- Layers: Neural networks are organized into layers: input, hidden (one or more), and output.
- Weights and Biases: Numbers assigned to connections between neurons. Weights determine the strength of connections, biases influence neuron activation.
- Activation Functions: Mathematical functions that determine the output of a neuron based on its input. Examples include sigmoid and ReLU.
- Learning: Neural networks learn by adjusting their weights and biases based on input data and desired outputs through algorithms like backpropagation.
How It Works / Its Function
Neural networks process information by feeding input data through layers of interconnected neurons. Each neuron performs a weighted sum of its inputs, adds a bias, and applies an activation function. The output of one layer becomes the input for the next, until a final output is produced. Learning involves iteratively adjusting the weights and biases to minimize the difference between predicted and actual outputs.
Examples
- Image Recognition: Identifying objects in images.
- Natural Language Processing: Understanding and generating human language.
- Medical Diagnosis: Assisting doctors in diagnosing diseases.
Why is it Important? / Significance
Neural networks are transforming many fields due to their ability to learn complex patterns from data. They’re crucial for advancements in AI, enabling breakthroughs in areas like image recognition, natural language processing, and robotics.
Their adaptability and power make them invaluable tools for solving intricate problems that traditional programming approaches struggle with.
Related Concepts
- Machine Learning
- Deep Learning
- Artificial Intelligence