Regularization
Prevent overfitting
- Regularization techniques like L1 and L2 add penalties to model complexity to improve generalization.
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Data Preprocessing
Preparing data for modeling
- Data Preprocessing includes cleaning, transforming, and normalizing data to improve model performance.
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Dropout
Prevent overfitting in neural networks
- Dropout randomly removes neurons during training to reduce overfitting and improve model robustness.
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Model Evaluation
Assessing model performance
- Model Evaluation uses metrics like accuracy, precision, and recall to measure how well a model performs.
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Batch Normalization
Normalize inputs in neural networks
- Batch Normalization stabilizes and accelerates training by normalizing layer inputs.
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Confusion Matrix
Visualize classification performance
- A Confusion Matrix shows true positives, false positives, true negatives, and false negatives for classification models.
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Data Augmentation
Increase dataset size artificially
- Data Augmentation creates modified versions of data to improve model robustness and performance.
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ROC Curve
Evaluate classification thresholds
- ROC Curve plots true positive rate against false positive rate to assess model performance across thresholds.
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Word Embeddings
Represent words as vectors
- Word Embeddings like Word2Vec and GloVe capture semantic meaning by representing words in continuous vector space.
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