Self-Supervised Learning
Self-supervised learning is a training approach where models generate their own supervisory signals from unlabeled data. Pre-training large language models with next-token prediction is a form of self-supervised learning.
Understanding Self-Supervised Learning
Self-supervised learning is a training paradigm where models generate their own supervisory signals from unlabeled data, eliminating the need for expensive human annotation. The model creates pretext tasks from the data itself, such as predicting masked words in a sentence (as in BERT) or predicting the next token (as in GPT). This approach has become the dominant pre-training strategy for large language models and has expanded into computer vision through methods like contrastive learning and masked autoencoders. Self-supervised learning bridges the gap between supervised learning and unsupervised learning, leveraging vast amounts of readily available unlabeled data to learn rich representations. After pre-training, these models are typically adapted to downstream tasks through fine-tuning or transfer learning with relatively small labeled datasets.
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.