Will the neural network algorithm always converge?

On page 231 of Neural Networks (by Haykin), he states that back propagation always converges, although the rate can be (in his words) “excruciatingly slow.”

What does it mean for a neural network to converge?

In the context of conventional artificial neural networks convergence describes a progression towards a network state where the network has learned to properly respond to a set of training patterns within some margin of error.

Will a neural net learn anything if all the weights are initialized with the same value?

Because if you initialize all weights to zero then all of the hidden neurons(units) in your neural network will be doing the exact same calculations.

Why do the initial weights for a neural network need to be small and random?

The weights of artificial neural networks must be initialized to small random numbers. This is because this is an expectation of the stochastic optimization algorithm used to train the model, called stochastic gradient descent. About the need for nondeterministic and randomized algorithms for challenging problems.

Why does my model not converge?

Lack of convergence is an indication that the data do not fit the model well, because there are too many poorly fitting observations. A data set showing lack of convergence can usually be rescued by setting aside for separate study the person or item performances which contain these unexpected responses.

Will perceptron algorithm always converge?

Yes, the perceptron learning algorithm is a linear classifier. If your data is separable by a hyperplane, then the perceptron will always converge. It will never converge if the data is not linearly separable.

What does it mean for a model to converge?

A machine learning model reaches convergence when it achieves a state during training in which loss settles to within an error range around the final value. In other words, a model converges when additional training will not improve the model.

What happens if you initialize the weights of a neural network to zero?

Initializing all the weights with zeros leads the neurons to learn the same features during training. Thus, both neurons will evolve symmetrically throughout training, effectively preventing different neurons from learning different things.

Why neurons should not all be initialized for all weights to the same value?

The weights attached to the same neuron, continue to remain the same throughout the training. It makes the hidden units symmetric and this problem is known as the symmetry problem. Hence to break this symmetry the weights connected to the same neuron should not be initialized to the same value.

Why weights are used in neural networks?

Weights(Parameters) — A weight represent the strength of the connection between units. If the weight from node 1 to node 2 has greater magnitude, it means that neuron 1 has greater influence over neuron 2. A weight brings down the importance of the input value.

How weights are assigned in neural networks?

Weight is the parameter within a neural network that transforms input data within the network’s hidden layers. As an input enters the node, it gets multiplied by a weight value and the resulting output is either observed, or passed to the next layer in the neural network.

What is convergence in modeling?

The “convergence model,” as it is known, describes communication, not as an event but a process. The sender and receiver engage in interpretation and response toward the goal of mutual understanding. Meaning is something that gets worked out by the sender and receiver.

Why does my neural network take so long to converge?

If your network takes really long to converge, and you are using some form of stochastic gradient descent (or mini-batch) then it could be the case that your network is in a plateau (a region where the energy/error function is very flat so that gradients are very low and thus convergence).

What is the advantage of normalizing the data?

Normalizing the data generally speeds up learning and leads to faster convergence. Also, the (logistic) sigmoid function is hardly ever used anymore as an activation function in hidden layers of Neural Networks, because the tanh function (among others) seems to be strictly superior.

How many nodes are there in a neural network?

They are around 230 nodes in the input layer, 9 nodes in the hidden layer and 1 output node in the output layer. I wanted to know, if I do early stopping condition (say stopping my neural network training after 100 iteration).

What are the best practices for training a neural network?

Among the best practices for training a Neural Network is to normalize your data to obtain a mean close to 0. Normalizing the data generally speeds up learning and leads to faster convergence.