Parameter
A parameter is a learnable variable within a model that is adjusted during training, such as weights and biases in a neural network. Large language models contain billions of parameters.
Understanding Parameter
A parameter in machine learning is a numerical value within a model that is learned from training data and determines how inputs are transformed into predictions. In a neural network, parameters include the weights connecting neurons across layers and the bias terms added at each node. Large language models like GPT-4 contain hundreds of billions of parameters, and the sheer scale of these models is a major driver of their impressive capabilities. During training, optimization algorithms like Adam adjust parameters through backpropagation to minimize the loss function. Parameters are distinct from hyperparameters, which are set before training begins and control the learning process itself, such as learning rate and batch size. The number of parameters in a model roughly indicates its capacity to learn complex patterns, though more parameters also increase the risk of overfitting and demand greater computational resources.
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.