✅ Deep Learning: Part 2 – Key Concepts in Neural Network Training 🧠⚙️

To train neural networks effectively, you must understand how they learn and where they can fail.

1️⃣ Epochs, Batches & Iterations
• Epoch – One full pass through the training data
• Batch size – Number of samples processed before weights are updated
• Iteration – One update step = 1 batch

Example:
If you have 1000 samples, batch size = 100 → 1 epoch = 10 iterations

2️⃣ Loss Functions
Measure how wrong predictions are.
• MSE (Mean Squared Error) – For regression
• Binary Cross Entropy – For binary classification
• Categorical Cross Entropy – For multi-class problems

3️⃣ Optimizers
Decide how weights are updated.
• SGD – Simple but may be slow
• Adam – Adaptive, widely used, faster convergence
• RMSprop – Good for RNNs or noisy data

4️⃣ Overfitting & Underfitting
• Overfitting – Model memorizes training data but fails on new data
• Underfitting – Model is too simple to learn the data patterns

How to Prevent Overfitting
✔️ Use more data
✔️ Add dropout layers
✔️ Apply regularization (L1/L2)
✔️ Early stopping
✔️ Data augmentation (for images)

5️⃣ Evaluation Metrics
• Accuracy – Overall correctness
• Precision, Recall, F1 – For imbalanced classes
• AUC – How well model ranks predictions

🧪 Try This:
Build a neural net using Keras
• Add 2 hidden layers
• Use Adam optimizer
• Train for 20 epochs
• Plot training vs validation loss

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