AI Terms

Here are some common AI terms used:

  1. Artificial intelligence (AI): A field of computer science that aims to create intelligent machines that can perform tasks that normally require human intelligence, such as learning, problem-solving, and decision-making.
  2. Deep learning: A subfield of machine learning that involves training artificial neural networks on large datasets to recognize patterns and make decisions.
  3. Machine learning: A field of computer science that involves the development of algorithms that can learn from data and improve their performance over time.
  4. Neural network: A type of machine learning model that is inspired by the structure and function of the human brain, and is composed of interconnected layers of artificial neurons.
  5. Natural language processing (NLP): A subfield of AI that focuses on enabling computers to understand, interpret, and generate human language.
  6. Computer vision: A subfield of AI that involves the development of algorithms and systems that can analyze and understand visual data, such as images and video.
  7. Decision tree: A type of machine learning model that involves constructing a tree-like structure of decisions and their potential consequences, used for classification and prediction tasks.
  8. Random forest: A type of machine learning model that involves training multiple decision trees on random subsets of the data and aggregating their predictions through voting or averaging
  9. Decision tree: A type of machine learning model that involves constructing a tree-like structure of decisions and their potential consequences, used for classification and prediction tasks.
  10. Random forest: A type of machine learning model that involves training multiple decision trees on random subsets of the data and aggregating their predictions, used for classification and regression tasks.
  11. Support vector machine (SVM): A type of machine learning model that involves finding the hyperplane in a high-dimensional space that maximally separates different classes, used for classification and regression tasks.
  12. K-nearest neighbors (KNN): A type of machine learning model that involves classifying a sample based on its proximity to the k most similar samples in the training dataset, used for classification and regression tasks.
  13. Naive Bayes: A type of machine learning model that involves calculating the probability of a sample belonging to each class based on the probability of each feature given the class, used for classification tasks.
  14. Logistic regression: A type of machine learning model that involves predicting the probability of a binary outcome based on a linear combination of the input features, used for classification tasks.
  15. Linear regression: A type of machine learning model that involves predicting a continuous outcome based on a linear combination of the input features, used for regression tasks.
  16. Gradient descent: An optimization algorithm that involves iteratively adjusting the parameters of a machine learning model
  17. Stochastic gradient descent (SGD): An optimization algorithm that involves updating the parameters of a machine learning model using a randomly selected subset of the training data, rather than the full dataset.
  18. Mini-batch gradient descent: An optimization algorithm that involves updating the parameters of a machine learning model using a small batch of the training data, rather than the full dataset or a single sample.
  19. Backpropagation: An algorithm that is used to train artificial neural networks by propagating the error gradient back through the network to update the weights.
  20. Overfitting: A phenomenon in machine learning where a model performs well on the training data but poorly on unseen data, due to excessive complexity or lack of generalization.
  21. Underfitting: A phenomenon in machine learning where a model performs poorly on both the training and unseen data, due to insufficient complexity or capacity.
  22. Regularization: A technique used to prevent overfitting by adding a penalty term to the objective function, which encourages the model to have simpler and more generalizable solutions.
  23. Hyperparameter: A parameter of a machine learning model that is set prior to training, and has a significant impact on the model’s performance.
  24. Cross-validation: A technique used to evaluate the performance of a machine learning model by training it on different subsets of the data and averaging the results.
  25. Feature engineering: The process of selecting, creating, and transforming the input features of a machine learning model to improve its performance.
  26. Feature selection: The process of selecting a subset of the input features of a machine learning model based on their importance or relevance to the task.
  27. Dimensionality reduction: The process of reducing the number of input features
  28. Model selection: The process of choosing the most appropriate machine learning model for a given task, based on its performance on a validation dataset.
  29. Ensemble learning: A technique that involves training multiple models and combining their predictions to improve the overall performance, such as through voting or averaging.
  30. Transfer learning: A technique that involves using a pre-trained machine learning model as a starting point for a new task, and fine-tuning it on the new data. This can significantly reduce the amount of training data and computational resources needed for the new task.