Why is it hard to train a neural network?
The iterative training process of neural networks solves an optimization problem that finds for parameters (model weights) that result in a minimum error or loss when evaluating the examples in the training dataset.
What do you need to know about back propagation neural network?
Before we learn Back Propagation Neural Network (BPNN), let’s understand: What is Artificial Neural Networks? A neural network is a group of connected I/O units where each connection has a weight associated with its computer programs. It helps you to build predictive models from large databases. This model builds upon the human nervous system.
Can a neural network have both high and low learning rates?
There are caveats for using both high and low learning rates. If a neural network’s learning rate is too high then it will quickly adjust it weights for any errors in the output. As a consequence, the latest data set in the training data set will have a much higher bearing on the network’s output than the earlier data sets.
Is the optimization problem solved by training a neural network?
Mathematically, the optimization problem solved by training a neural network is referred to as NP-complete (e.g. they are very hard to solve). We prove this problem NP-complete and thus demonstrate that learning in neural networks has no efficient general solution. — Neural Network Design and the Complexity of Learning, 1988.
Why is reducing the costs of training neural networks remains a challenge?
While AI researchers have made progress in reducing the costs of running deep learning models, the larger problem of reducing the costs of training deep neural networks remains unsolved.
Is there an algorithm to train a neural network?
In fact, there does not exist an algorithm to solve the problem of finding an optimal set of weights for a neural network in polynomial time. Mathematically, the optimization problem solved by training a neural network is referred to as NP-complete (e.g. they are very hard to solve).
How to improve the accuracy of neural networks?
In the process of training, we want to start with a bad performing neural network and wind up with network with high accuracy. In terms of loss function, we want our loss function to much lower in the end of training. Improving the network is possible, because we can change its function by adjusting weights.