Understanding Neural Networks
Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data. As the beating heart of many machine learning systems, they are composed of layers of nodes, similar to the neurons in a biological brain. 🧠
Key Concepts
- Neurons: Basic units of a neural network, processing data inputs and passing the information to other neurons.
- Layers: Arrange neurons in layers, such as input, hidden, and output layers. Each layer has specific roles and responsibilities in data processing.
- Activation Functions: Determine the output of a node. Common functions include sigmoid, tanh, and ReLU.
Applications
Neural networks are employed across numerous industries, revolutionizing fields such as:
- Healthcare: Analyzing medical records and predicting patient outcomes.
- Finance: Automating trading systems and detecting fraudulent activities.
- Technology: Powering complex tasks like natural language processing and image recognition.
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