ModelRefs / Introduction to Neural Networks — Tutorial
Introduction to Neural Networks — Tutorial
Understanding the building blocks of deep learning. Covers What are Neural Networks, How Neurons Work, Activation Functions.
Overview
Understanding the building blocks of deep learning
Level: Intermediate. Estimated reading time: 30 minutes.
What are Neural Networks?
Neural Networks are computing systems inspired by the biological neural networks in animal brains. They consist of interconnected nodes (neurons) organized in layers that can learn to recognize patterns.
Key components: • Input Layer: Receives the data • Hidden Layers: Process and transform the data • Output Layer: Produces the final prediction
How Neurons Work
Each neuron: 1. Receives inputs from previous layer 2. Multiplies each input by a weight 3. Adds all weighted inputs plus a bias 4. Applies an activation function 5. Passes output to next layer
The network learns by adjusting weights and biases.
Activation Functions
Common activation functions: • ReLU: max(0, x) - most common in hidden layers • Sigmoid: 1/(1+e^-x) - outputs between 0 and 1 • Tanh: outputs between -1 and 1 • Softmax: converts to probabilities for classification
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