Deep learningBranch of machine learningRepresenting images on multiple layers of abstraction in deep learningIn machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" them to process data.
Adversarial machine learningResearch field that lies at the intersection of machine learning and computer securityAdversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. Machine learning techniques are mostly designed to work on specific problem sets, under the assumption that the training and test data are generated from the same statistical distribution (IID).
Text-to-image modelA text-to-image (T2I or TTI) model is a machine learning model which takes an input natural language prompt and produces an image matching that description. Text-to-image models gradually began to be developed in the mid-2010s during the beginnings of the AI boom, as a result of advances in deep neural networks.
Machine learningSubset of artificial intelligenceMachine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without being explicitly programmed. Statistics and mathematical optimisation methods compose the foundations of machine learning.
MalwareMalware (a portmanteau of malicious software) is any software intentionally designed to cause disruption or destruction to a computer, server, client, or computer network, leak private information, gain unauthorized access to information or systems, deprive access to information, or interfere with the user's computer security and privacy without their knowledge.
Support vector machineIn machine learning, a support vector machine (SVM) or support vector network is a supervisedmax-margin model with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories, SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).
Byzantine faultA Byzantine fault is a condition of a system, particularly a distributed computing system, where a fault occurs such that different symptoms are presented to different observers, including imperfect information on whether a system component has failed.
Training, validation, and test data setsIn machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets.
Independent and identically distributed random variablesIn probability theory and statistics, a collection of random variables is independent and identically distributed (i.i.d., iid, or IID) if each random variable has the same probability distribution as the others and all are mutually independent. IID was first defined in statistics and finds application in many fields, such as data mining and signal processing.*As an Amazon Associate I earn from qualifying purchases.