Transfer Learning
Transfer learning is the technique of applying knowledge gained from training on one task to improve performance on a different but related task. It enables powerful AI models from limited domain-specific data by leveraging pre-trained knowledge.
Understanding Transfer Learning
Transfer learning is the technique of taking a model trained on one task or domain and adapting it to a different but related task, dramatically reducing the data and compute required for the new application. It is the driving force behind modern AI's accessibility, enabling practitioners to fine-tune large pre-trained models like BERT, GPT, or ResNet on domain-specific tasks with relatively small datasets. In computer vision, models pre-trained on ImageNet learn general visual features that transfer effectively to medical imaging, satellite analysis, or manufacturing quality control. In natural language processing, foundation models pre-trained through self-supervised learning on massive text corpora capture linguistic knowledge that transfers to sentiment analysis, question answering, and text classification. Transfer learning has made state-of-the-art AI achievable without the enormous computational budgets required for training from scratch.
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.