Contents
Which is the best way to design a neural network?
In general, using the same number of neurons for all hidden layers will suffice. For some datasets, having a large first layer and following it up with smaller layers will lead to better performance as the first layer can learn a lot of lower-level features that can feed into a few higher order features in the subsequent layers.
How to reduce the size of a neural network?
An approach to counteract this is to start with a huge number of hidden layers + hidden neurons and then use dropout and early stopping to let the neural network size itself down for you.
How are pre trained models used in neural networks?
For these use cases, there are pre-trained models ( YOLO, ResNet, VGG) that allow you to use large parts of their networks, and train your model on top of these networks to learn only the higher order features. In this case, your model will still have only a few layers to train.
Why are there so many hidden layers in neural networks?
The number of hidden layers is highly dependent on the problem and the architecture of your neural network. You’re essentially trying to Goldilocks your way into the perfect neural network architecture — not too big, not too small, just right.
Why do not all neural networks learn the same way?
Just like people, not all neural network layers learn at the same speed. So when the backprop algorithm propagates the error gradient from the output layer to the first layers, the gradients get smaller and smaller until they’re almost negligible when they reach the first layers.
Where does the computation take place in a neural network?
The main computation of a Neural Network takes place in the hidden layers. So, the hidden layer takes all the inputs from the input layer and performs the necessary calculation to generate a result. This result is then forwarded to the output layer so that the user can view the result of the computation.
How many types of neural networks are there?
The zoo of neural network types grows exponentially. One needs a map to navigate between many emerging architectures and approaches. Fortunately, Fjodor van Veen from Asimov institute compiled a wonderful cheatsheet on NN topologies.