Contents

- 1 Why are neural networks initialised with random weights?
- 2 What is epoch in deep learning?
- 3 Can you use the same weights every time you train a neural network?
- 4 Why do nodes have different weights in a neural network?
- 5 Why do neural networks have to be initialized to random numbers?
- 6 Which is the best way to evaluate a neural network?

## Why are neural networks initialised with random weights?

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.

## What is epoch in deep learning?

An epoch is a term used in machine learning and indicates the number of passes of the entire training dataset the machine learning algorithm has completed. Datasets are usually grouped into batches (especially when the amount of data is very large). Many models are created with more than one epoch.

## Can you use the same weights every time you train a neural network?

We can use the same set of weights each time we train the network; for example, you could use the values of 0.0 for all weights. In this case, the equations of the learning algorithm would fail to make any changes to the network weights, and the model will be stuck.

## Why do nodes have different weights in a neural network?

Specifically, nodes that are side-by-side in a hidden layer connected to the same inputs must have different weights for the learning algorithm to update the weights. This is often referred to as the need to break symmetry during training.

## Why do neural networks have to be initialized to random numbers?

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. Earn an MBA Online.

## Which is the best way to evaluate a neural network?

The most effective way to evaluate the skill of a neural network configuration is to repeat the search process multiple times and report the average performance of the model over those repeats. This gives the configuration the best chance to search the space from multiple different sets of initial conditions.