Continual Learning
Continual learning is the ability of an AI system to learn new tasks or knowledge over time without forgetting previously learned information. It aims to create more human-like learning that accumulates knowledge incrementally.
Understanding Continual Learning
Continual learning, also known as lifelong learning, is a machine learning paradigm where models incrementally acquire new knowledge over time without forgetting previously learned information. This contrasts with traditional training approaches where a model is trained once on a static dataset. In real-world deployment, data distributions constantly shift—new product categories appear in e-commerce, medical research uncovers new conditions, and user preferences evolve. Continual learning systems must balance plasticity (the ability to learn new things) with stability (retaining old knowledge), directly confronting the challenge of catastrophic forgetting. Techniques include regularization-based methods, memory replay buffers, and dynamic architecture expansion. This paradigm is critical for applications like autonomous driving, personalized recommendation systems, and any model serving environment where retraining from scratch is impractical.
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.