Epoch
An epoch is one complete pass through the entire training dataset during model training. Training typically requires multiple epochs for the model to converge to good performance.
Understanding Epoch
An epoch represents one complete pass through the entire training dataset during model training. In a typical deep learning workflow, training involves multiple epochs, with the model's parameters updated via gradient descent after processing each batch within an epoch. The number of epochs is a key hyperparameter: too few epochs can lead to underfitting where the model has not learned enough patterns, while too many can cause overfitting where the model memorizes training data rather than generalizing. Practitioners monitor evaluation metrics like loss and accuracy on a validation set after each epoch to determine when to stop training, a technique called early stopping. Learning rate schedulers often adjust the learning rate across epochs. The optimal number of epochs varies by dataset size, model complexity, and whether techniques like data augmentation and dropout are used.
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.