Principal Component Analysis (PCA) is a statistical method. A set of variables that may be related to each other is transformed into a set of linearly related variables by orthogonal transformation The converted set of variables is called the principal component

About Bayesian formula- P(WlX)=P(XlW)*P(W)/P(X) What is the correct description?

enter 32*32 Image with size 5*5 The step size of the convolution kernel is 1 Convolution calculation, output image Size is:

TensorFlow2.0 not support tensor in GPU with CPU Transfer between each other.

In the process of training the neural network, we use the gradient descent method to continuously update which value, which makes the loss Function minimization?