Zero-Shot Learning
Zero-shot learning is the ability of a model to correctly handle tasks or recognize classes it has never been explicitly trained on. Large language models demonstrate strong zero-shot capabilities across diverse tasks.
Understanding Zero-Shot Learning
Zero-shot learning enables AI models to recognize or classify concepts they have never explicitly seen during training, leveraging learned semantic relationships to generalize to novel categories. The model transfers knowledge from seen classes to unseen ones using shared attributes, textual descriptions, or embedding spaces that bridge familiar and unfamiliar concepts. For example, a model trained on images of horses and text descriptions of zebras might recognize a zebra by combining its visual knowledge of horses with the semantic concept of stripes. In natural language processing, large language models exhibit powerful zero-shot capabilities across diverse tasks through their broad pre-training on internet-scale text data. Zero-shot learning reduces the dependency on labeled training data and enables rapid deployment for new categories, making it especially valuable when annotation is expensive or when new classes emerge frequently.
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.