How can you improve the accuracy of a neural network model?

Now we’ll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:

1. Increase hidden Layers.
2. Change Activation function.
3. Change Activation function in Output layer.
4. Increase number of neurons.
5. Weight initialization.
6. More data.
7. Normalizing/Scaling data.

What increases the complexity of a neural network model?

Increasing the depth increases the capacity of the model. Training deep models, e.g. those with many hidden layers, can be computationally more efficient than training a single layer network with a vast number of nodes.

Which of the following techniques can be used to prevent overfitting in a neural network?

Regularization methods like weight decay provide an easy way to control overfitting for large neural network models. A modern recommendation for regularization is to use early stopping with dropout and a weight constraint.

How can we reduce computational complexity of neural network?

Reducing computational complexity: Network Compression and Acceleration. This body of work primarily focuses on taking a trained neural network and sparsifying or otherwise compressing the connections in the network, so that it requires less computation to use it in prediction tasks.

Do you need to read the essay on computational complexity?

This essay assumes familiarity with analytical complexity analysis of algorithms, and hereunder big-O notation. If you need a recap, you should read the essay on computational complexity before continuing. Looking at inference part of a feed forward neural network, we have forward propagation.

What is neural network compression?

This body of work primarily focuses on taking a trained neural network and sparsifying or otherwise compressing the connections in the network, so that it requires less computation to use it in prediction tasks. This is typically done by using optimization or heuristics such as “pruning”, quantizing, or low-rank compression.

What is the best run-time for neural networks?

[3] The best run-time for neural networks is an area of active research. O (n^4) O(n4). We have considered the large constant factor of gradient descent required to reach an acceptable accuracy which strengthens the argument.