🎯 Training Neural Networks

Training a neural network means teaching it to make better predictions by adjusting its weights and biases through many iterations.

🔁 1. Training Process Overview

  • 🔧 Initialize weights – random small values
  • ➡️ Forward Propagation – get predictions
  • 📉 Calculate Loss – measure how wrong it is
  • 🔄 Backward Propagation – find how to adjust weights
  • ⚙️ Update weights – using an optimization algorithm
  • 🔁 Repeat – for many examples and iterations (epochs)

⚙️ 2. Optimization Algorithms (Gradient Descent)

🔽 Gradient Descent Moves the weights in the direction that reduces loss. Like walking downhill to reach the lowest point (minimum loss).
📊 Optimizer Comparison
Optimizer Feature
SGD (Stochastic Gradient Descent) Updates weights using one data point at a time (fast but noisy)
Mini-batch GD Uses small batches (common in practice)
Adam Adaptive optimizer; combines momentum + scaling (very popular)

⚠️ 3. Overfitting vs Underfitting

Term What Happens Example
Overfitting Model learns training data too well, including noise → poor generalization High accuracy on training, low on test
Underfitting Model is too simple → can’t capture patterns Low accuracy on both

🛡️ 4. Regularization Techniques

Help prevent overfitting:
Method Description
L1 / L2 Regularization Add penalty to large weights in loss function
Dropout Randomly turns off some neurons during training
Early Stopping Stop training when validation loss starts increasing

⚙️ 5. Hyperparameters to Tune

Key Hyperparameters in Deep Learning:
Parameter Description
Learning Rate Size of the steps in gradient descent
Epochs Full passes through training data
Batch Size Number of samples processed at once
Number of Layers/Neurons Model complexity

🧾 Summary

  • Neural networks learn by adjusting weights to minimize loss.
  • Optimizers like Adam improve the speed and stability of training.
  • Proper tuning and regularization help avoid overfitting.

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