A neural network is a computational model inspired by biological neural networks that learns patterns in data through interconnected nodes (neurons) organized in layers.
Neural networks consist of input layers, hidden layers, and output layers. Each neuron receives weighted inputs, applies an activation function, and passes the result forward. During training, the network adjusts weights through backpropagation to minimize prediction errors.
The network transforms input data through mathematical operations at each layer, allowing it to learn complex non-linear relationships. Forward propagation moves data through the network to generate predictions, while backward propagation updates weights based on prediction errors.
For a single neuron:
Where:
A neural network for credit scoring might have inputs like income, credit history, and debt ratio. Hidden layers process these features to identify complex patterns indicating creditworthiness. The output layer produces a probability of default.
1import tensorflow as tf
2
3model = tf.keras.Sequential([
4 tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)),
5 tf.keras.layers.Dense(32, activation='relu'),
6 tf.keras.layers.Dense(1, activation='sigmoid')
7])
8