Coding and Engineering Concepts

Regularization

Prevent overfitting

Regularization techniques like L1 and L2 add penalties to model complexity to improve generalization.

Preparing data for modeling

Data Preprocessing includes cleaning, transforming, and normalizing data to improve model performance.

Prevent overfitting in neural networks

Dropout randomly removes neurons during training to reduce overfitting and improve model robustness.

Assessing model performance

Model Evaluation uses metrics like accuracy, precision, and recall to measure how well a model performs.

Normalize inputs in neural networks

Batch Normalization stabilizes and accelerates training by normalizing layer inputs.

Visualize classification performance

A Confusion Matrix shows true positives, false positives, true negatives, and false negatives for classification models.

Increase dataset size artificially

Data Augmentation creates modified versions of data to improve model robustness and performance.

Evaluate classification thresholds

ROC Curve plots true positive rate against false positive rate to assess model performance across thresholds.

Represent words as vectors

Word Embeddings like Word2Vec and GloVe capture semantic meaning by representing words in continuous vector space.