Few-Shot Learning
Few-shot learning is the ability of a model to learn and generalize from only a small number of labeled examples. Large language models demonstrate impressive few-shot capabilities through in-context learning.
Understanding Few-Shot Learning
Few-shot learning is a machine learning paradigm where models are designed to generalize from only a handful of labeled examples per class, rather than requiring large annotated datasets. This capability is especially important in domains where data labeling is expensive or rare, such as medical imaging, wildlife identification, and industrial defect detection. Approaches include metric-based methods that learn similarity functions in an embedding space, optimization-based methods like MAML that train models to adapt quickly, and large language models like GPT that achieve few-shot performance through in-context learning with carefully crafted prompts. Few-shot learning is closely related to transfer learning, where pre-trained foundation models provide strong initial representations. The paradigm challenges traditional supervised learning assumptions and is pushing the field toward more sample-efficient AI systems that better mimic human learning abilities.
Category
Machine Learning
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Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.