Hyperparameter tuning – the process of selecting the best values for the parameters of a machine learning algorithm.
Quantum computing – a field of computing that utilizes the principles of quantum mechanics to perform complex calculations.
Blockchain – a digital ledger of transactions that is maintained by a network of computers, with each transaction being verified by a consensus of network participants.
Convolutional neural network – a type of artificial neural network commonly used in image and video recognition.
Monte Carlo simulation – a computational method that uses random sampling to simulate the behavior of complex systems.
Differential equations – a mathematical tool for modeling continuous processes, such as those in physics and engineering.
Singular value decomposition – a matrix factorization technique that is commonly used in machine learning and data analysis.
Discrete Fourier transform – a mathematical transform used to convert a time-domain signal into a frequency-domain representation.
Stochastic gradient descent – an optimization algorithm commonly used in machine learning for minimizing the cost function of a model.
K-nearest neighbors – a classification algorithm that assigns a new data point to the class of its nearest neighbors in a feature space.
