The vector of partial derivatives with respect to all of the independent variables. In machine learning , the gradient is the vector of partial derivatives of the model function. The gradient points in the direction of steepest ascent.1
목차 목차 관련 글 Nested Learning: The Illusion of Deep Learning Architectures “In addition to its neuroscientifically plausible and mathematically white-box nature, we advocate for its impor…” Clipping (machine learning) 이 글을 인용: “Gradient clipping forces gradient values within a designated range during training.” ML crash course - Linear regression “Define and describe how gradient descent finds the optimal model parameters.” Vanishing gradient problem 이 글을 인용: “Models suffering from the vanishing gradient problem become difficult or impossible to train.” 역전파 “That is, backpropagation calculates the partial derivative of the error with respect to each parameter.” Exploding gradient problem 이 글을 인용: “The tendency for gradient in DNNs (especially RNNs) to become surprisingly steep (high).” 경사하강법 “Gradient descent is older--much, much older--than machine learning.” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] Gradient boosting “Gradient boosting is a powerful ensemble machine learning technique used for both regression and classification…” Convex function 관련 절: See also Machine Learning Systems “Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML…” Learning rate “A floating-point number that tells the gradient descent algorithm how strongly to adjust weights and biases on …” 배치 크기 “Mini-batch stochastic gradient descent, in which the batch size is usually between 10 and 1000.” Gradient clipping “A commonly used mechanism to mitigate the exploding gradient problem by artificially limiting (clipping) the ma…” Epoch (machine learning) “The number of epochs is a hyperparameter you set before the model begins training.” Iteration (machine learning) “A backward pass (backpropagation) to adjust the model's parameters based on the loss and the learning rate.” Generalization (machine learning) “To encourage generalization, regularization helps a model train less exactly to the peculiarities of the data i…” Downsampling (machine learning) “Reducing the amount of information in a feature in order to train a model more efficiently.” 모델 (기계학습) “Unsupervised machine learning also generates models, typically a function that can map an input example to the …” 지도 학습 “Supervised machine learning is analogous to learning a subject by studying a set of questions and their corresp…”