Practice Questions: ML Foundations & Deep Learning

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1. Generalization and Overfitting [Generalization / Train-Test Split]

2. Cross-Entropy Loss Computation [Cross-Entropy Loss]

3. Softmax Calculation [Softmax]

4. Backpropagation Through a Small Network [Backpropagation]

5. CNN Output Size Calculation [CNN Convolution Output Size]

6. CNN Parameter Counting [CNN Parameters]

7. RNN vs LSTM: Vanishing Gradients [RNN / LSTM / Vanishing Gradients]

8. GNN Neighbor Aggregation [GCN Equation]

9. Data Augmentation [Data Augmentation Purpose and Techniques]

10. Comparative: CNN vs RNN vs GNN [Architecture Comparison]