Normalization
Normalization is the process of scaling input features to a standard range or distribution to improve model training. Common techniques include min-max scaling, z-score standardization, and layer normalization.
Understanding Normalization
Normalization is a family of techniques used to scale and standardize data or intermediate network activations to improve the training stability and performance of machine learning models. Common data normalization methods include min-max scaling, which maps values to a fixed range, and z-score standardization, which centers data around zero with unit variance. Within deep learning, batch normalization standardizes layer inputs across mini-batches, while layer normalization operates across features within each individual example, making it preferred in transformer architectures. These techniques address the problem of internal covariate shift, where the distribution of layer inputs changes during training, slowing convergence and making optimization more difficult. Proper normalization enables the use of higher learning rates and reduces sensitivity to parameter initialization, making it a standard component in modern neural network pipelines for tasks ranging from image classification to natural language processing.
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Data Science
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A/B Testing
A/B testing is an experimental method that compares two versions of a model, prompt, or interface to determine which performs better. In AI, A/B testing helps evaluate model outputs, UI changes, and prompt strategies by measuring user engagement or accuracy.
Annotation
Annotation is the process of adding labels or metadata to raw data to create training datasets for supervised learning. Data annotation can involve labeling images, tagging text, or marking audio segments.
Benchmark
A benchmark is a standardized test or dataset used to evaluate and compare the performance of different AI models. Common benchmarks include MMLU, HumanEval, and ImageNet.
Causal Inference
Causal inference is the process of determining cause-and-effect relationships from data, going beyond mere correlation. AI systems increasingly use causal reasoning to make more robust and interpretable decisions.
Cross-Validation
Cross-validation is a model evaluation technique that splits data into multiple folds, training and testing on different subsets in rotation. K-fold cross-validation provides more reliable performance estimates than a single train-test split.
Data Augmentation
Data augmentation is a technique that artificially increases training dataset size by creating modified versions of existing data. In computer vision, this includes rotations, flips, and color changes; in NLP, it includes paraphrasing and synonym replacement.
Data Drift
Data drift occurs when the statistical properties of production data change over time compared to the training data. Drift can degrade model performance and requires monitoring and retraining strategies to address.
Data Labeling
Data labeling is the process of assigning meaningful tags or annotations to raw data to create supervised learning datasets. High-quality labeled data is essential for training accurate machine learning models.