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.
Understanding Data Augmentation
Data augmentation is a set of techniques used to artificially expand a training dataset by applying transformations to existing samples, thereby improving model robustness and reducing overfitting. In computer vision, common augmentations include rotation, flipping, cropping, color jittering, and adding noise to images, which help convolutional neural networks learn invariant features. In natural language processing, augmentation strategies include synonym replacement, back-translation, and random insertion or deletion of words within a corpus. More advanced approaches use generative AI models like GANs or diffusion models to synthesize entirely new training examples. Data augmentation is especially valuable when data labeling is expensive or when working with small datasets. It has become a standard component of modern deep learning pipelines across domains like healthcare imaging and speech recognition.
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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 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.
Dimensionality Reduction
Dimensionality reduction is the process of reducing the number of features in a dataset while preserving its essential structure. Techniques like PCA and t-SNE help with visualization, noise reduction, and computational efficiency.