Why it is at all required to choose different learning rates for different weights?

Generally, a large learning rate allows the model to learn faster, at the cost of arriving on a sub-optimal final set of weights. A smaller learning rate may allow the model to learn a more optimal or even globally optimal set of weights but may take significantly longer to train.

What is the significance of learning rate?

The learning rate controls how quickly the model is adapted to the problem. Smaller learning rates require more training epochs given the smaller changes made to the weights each update, whereas larger learning rates result in rapid changes and require fewer training epochs.

What will happen if the learning rate is set too low or too high?

If your learning rate is set too low, training will progress very slowly as you are making very tiny updates to the weights in your network. However, if your learning rate is set too high, it can cause undesirable divergent behavior in your loss function.

How does learning rate affect Overfitting?

Well adding more layers/neurons increases the chance of over-fitting. Therefore it would be better if you decrease the learning rate over time. Removing the subsampling layers also increases the number of parameters and again the chance to over-fit.

Does changing learning rate help overfitting?

It’s actually the OPPOSITE! A smaller learning rate will increase the risk of overfitting! Citing from Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates (Smith & Topin 2018) (a very interesting read btw):

What is lower learning rate?

both low and high learning rates results in wasted time and resources. A lower learning rate means more training time. more time results in increased cloud GPU costs. a higher rate could result in a model that might not be able to predict anything accurately.

How do learning rates affect the performance of machine learning models?

Effect of various learning rates on convergence (Img Credit: cs231n) Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy). Thus getting it right from the get go would mean lesser time for us to train the model. Less training time, lesser money spent on GPU cloud compute.

What happens if the learning rate is too small?

A learning rate that is too large can cause the model to converge too quickly to a suboptimal solution, whereas a learning rate that is too small can cause the process to get stuck. The challenge of training deep learning neural networks involves carefully selecting the learning rate.

Why is my machine learning model so warm?

The “warm” bit comes from the fact that when the learning rate is restarted, it does not start from scratch; but rather from the parameters to which the model converged during the last step [7]. While there are variations of this, the below diagram demonstrates one of its implementation, where each cycle is set to the same time period.

How are learning rates configured?

Typically learning rates are configured naively at random by the user. At best, the user would leverage on past experiences (or other types of learning material) to gain the intuition on what is the best value to use in setting learning rates.