How to build a neural network from scratch

Short answer

Define layers, weights, and biases; implement forward propagation, a loss function, and backpropagation to update weights via gradient descent. For preprocessing your data, see how to preprocess data for machine learning models. For evaluating the trained model, see how to evaluate machine learning model performance.

Steps

1. Initialize weights and biases randomly
2. Implement forward pass: dot product + activation
3. Compute loss with cross-entropy or MSE
4. Implement backward pass to calculate gradients
5. Update weights with gradient descent

Tips

• Start with a single hidden layer before going deep
• Use vectorization with NumPy for speed
• Understand the bias-variance tradeoff in machine learning when tuning architecture