Explanation:
All of the above statements are valid
Explanation:
All of the above
Explanation:
Bagging is the process of creating many predictors that perform similarly to a single predictor.
Explanation:
When batch normalization is applied to neural networks, the inputs to hidden layers are normalized, resulting in improved outcomes.
Explanation:
Dense Layer = Fullyconnected Layer = topology, describes how neurons are connected to the next layer of neurons (every neuron in the next layer is connected to every neuron in the next layer), an intermediate layer (also known as hidden layer, see figure) Output Layer = Last layer of a Multilayer Perceptron
Explanation:
The rectified linear activation function (ReLU) is a non-linear or piecewise linear function that outputs the input directly if it is positive and zero otherwise. It is the most often used activation function in neural networks, particularly Convolutional Neural Networks (CNNs) and Multilayer Perceptrons.
Explanation:
The feature maps' dimensions are reduced by combining layers. As a result, the number of parameters to learn and the quantity of computation done in the network are both reduced. The features contained in a region of the feature map created by a convolution layer are summarized by the pooling layer.
Explanation:
A recurrent neural network (RNN) is a form of artificial neural network that works with time series or sequential data.
Explanation:
The first layer to derive features from the input image is the convolutional layer. By learning visual attributes with a small square of input data, the convolutional layer preserves the link between pixels.
