The challenges of training neural networks
Training neural networks is a complex task and comes with challenges during the training, such as local minima and vanishing/exploding gradients, as well as computational costs and interpretability. All challenges are explained in detail in the following points:
- Local minima: The objective of training a neural network is to find the set of weights that minimizes the loss function. This is a high-dimensional optimization problem, and there are many points (sets of weights) where the loss function has local minima. A suboptimal local minimum is a point where the loss is lower than for the nearby points but higher than the global minimum, which is the overall lowest possible loss. The training process can get stuck in such suboptimal local minima. It’s important to remember that the local minima problem exists even in convex loss functions due to the discrete representation that is a part of digital computation.
- Vanishing/exploding...
