How to use data scaling to improve deep learning?
You must ensure that the scale of your output variable matches the scale of the activation function (transfer function) on the output layer of your network. If your output activation function has a range of [0,1], then obviously you must ensure that the target values lie within that range.
Are there loss functions that work for all kind of data?
There is not a single loss function that works for all kind of data. It depends on a number of factors including the presence of outliers, choice of machine learning algorithm, time efficiency of gradient descent, ease of finding the derivatives and confidence of predictions.
What does a loss function tell you about your model?
If your predictions are totally off, your loss function will output a higher number. If they’re pretty good, it’ll output a lower number. As you change pieces of your algorithm to try and improve your model, your loss function will tell you if you’re getting anywhere.
When do we need to use scaling in machine learning?
In many algorithms, when we desire faster convergence, scaling is a MUST like in Neural Network. Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions do not work correctly without normalization.
Why is feature scaling important in machine learning?
Feature scaling is essential for machine learning algorithms that calculate distances between data. If not scale, the feature with a higher value range starts dominating when calculating distances, as explained intuitively in the “why?” section.
How does a feature scaling estimator scale data?
Scale each feature by its maximum absolute value. This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set is 1.0. It does not shift/center the data and thus does not destroy any sparsity.
Why do we use feature scaling in neural network?
Another reason why feature scaling is applied is that few algorithms like Neural network gradient descent converge much faster with feature scaling than without it. Photo Credit. One more reason is saturation, like in the case of sigmoid activation in Neural Network, scaling would help not to saturate too fast.