Contents
What is the architecture of neural network?
Usually, a Neural Network consists of an input and output layer with one or multiple hidden layers within. In a Neural Network, all the neurons influence each other, and hence, they are all connected.
How do you measure neural network performance?
Many methods were implemented to measure the performance of neural networks such as MSE , NMSE , RMSE, R square for regression. And TP rate ,FP rate , F-measure , accuracy , precision and recall for classification.
How are size and depth of neural networks affected?
The size and depth of neural networks interact with other hyper-paramaters too, so that changing one thing elsewhere can affect where the best values are. So it is not possible to isolate a “best” size and depth for a network then continue to tune other parameters in isolation.
How to build an efficient neural network architecture?
1.Always start by using smaller filters is to collect as much local information as possible, and then gradually increase the filter width to reduce the generated feature space width to represent more global, high-level and representative information 2.
How is Relu improving the performance of neural networks?
But, they suffered from the problem of vanishing gradients, i.e during backpropagation, the gradients diminish in value when they reach the beginning layers. This stopped the neural network from scaling to bigger sizes with more layers. ReLU was able to overcome this problem and hence allowed neural networks to be of large sizes.
How are neural networks used in computer vision?
ResNets achieve excellent generalization performance on recognition tasks, and quickly became one of the most popular architectures in various computer vision tasks. Prior to the development of ResNets, stacking layers together to increase the depth of a neural network eventually caused the performance of the network to plateau or rapidly degrade.