What is sparse gradient in deep learning?

2, we describe the sparse gradient learning algorithm for regression, where an automatic variable selection scheme is introduced. The derived sparse gradient is an approximation of the true gradient of regression function under certain conditions, which we give in Sect. 2.3 and their proofs are delayed in Sect. 3.

What is sparse data in deep learning?

Features with sparse data are features that have mostly zero values. This is different from features with missing data. Examples of sparse features include vectors of one-hot-encoded words or counts of categorical data. On the other hand, features with dense data have predominantly non-zero values.

What does gradient mean in deep learning?

A gradient simply measures the change in all weights with regard to the change in error. You can also think of a gradient as the slope of a function. The higher the gradient, the steeper the slope and the faster a model can learn.

What is sparse data problem?

A common problem in machine learning is sparse data, which alters the performance of machine learning algorithms and their ability to calculate accurate predictions. Data is considered sparse when certain expected values in a dataset are missing, which is a common phenomenon in general large scaled data analysis.

What is sparse and dense data?

Typically, sparse data means that there are many gaps present in the data being recorded. Dense data can be described as many different pieces of the required information on a specific kind of a subject, no matter whatever the subject happens to be.

What is Optimisation in deep learning?

Optimization is the problem of finding a set of inputs to an objective function that results in a maximum or minimum function evaluation. It is the challenging problem that underlies many machine learning algorithms, from fitting logistic regression models to training artificial neural networks.

What is RMSprop in deep learning?

RMSprop is a gradient based optimization technique used in training neural networks. This normalization balances the step size (momentum), decreasing the step for large gradients to avoid exploding, and increasing the step for small gradients to avoid vanishing.

What is Optimizer in deep learning?

While training the deep learning model, we need to modify each epoch’s weights and minimize the loss function. An optimizer is a function or an algorithm that modifies the attributes of the neural network, such as weights and learning rate. Thus, it helps in reducing the overall loss and improve the accuracy.