What is backprop in neural network?

Backpropagation in neural network is a short form for “backward propagation of errors.” It is a standard method of training artificial neural networks. This method helps calculate the gradient of a loss function with respect to all the weights in the network.

What are some practical problems with the sigmoid activation function in neural nets?

The two major problems with sigmoid activation functions are: Sigmoid saturate and kill gradients: The output of sigmoid saturates (i.e. the curve becomes parallel to x-axis) for a large positive or large negative number. Thus, the gradient at these regions is almost zero.

What are the different activation functions in neural network?

3 Types of Neural Networks Activation Functions

• Binary Step Function.
• Linear Activation Function.
• Sigmoid/Logistic Activation Function.
• The derivative of the Sigmoid Activation Function.
• Tanh Function (Hyperbolic Tangent)
• Gradient of the Tanh Activation Function.
• ReLU Activation Function.
• The Dying ReLU problem.

Can all hard problems be handled by a multilayer feedforward neural network with nonlinear units?

1. Can all hard problems be handled by a multilayer feedforward neural network, with nonlinear units? Explanation: Multilayer perceptrons can deal with all hard problems.

What are activation functions in machine learning?

Simply put, an activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron.

Which is not activation function in neural networks?

A neural network without an activation function is essentially just a linear regression model. Thus we use a non linear transformation to the inputs of the neuron and this non-linearity in the network is introduced by an activation function.

Can all hard problems be handled by a multilayer feedforward?

Can all hard problems be handled by a multilayer feedforward neural network, with nonlinear units? Explanation: Multilayer perceptrons can deal with all hard problems.