Deep learning optimizer literature starts with Gradient Descent and the Stochastic Gradient Descent (SGD) is one very widely used version of it. The gradients are not calculated for the loss functions over all data points but over a randomly selected sub-sample. This is why it is also called mini-batch gradient descent sometimes.
SGD usually converges slower than Adam
, but generalize a bit better empirically speaking. So a good approach is to start with one of the Adam solvers and if you don't get good results, switch to SGD.
Most relevant hyper-parameters of SGD:
Hyperparameter tuning yields 1-3% marginal gains in performance. Fixing your data is usually more effective.
The goal of each solver is to find the loss function's minimum.
However, this cannot be done by just setting the derivative to 0 (as you
learned to do in calculus I) because there is no closed-form solution.
This is because the loss landscape of neural networks is highly
non-convex and riddled with saddle points.
Have you met Gradient Descent? Gradient Descent is
an algorithm that finds local minima. It calculates the gradient of a
given point on a loss function. If the gradient is negative, it updates
the weights moving to a point in the direction of the gradient; if it's
positive to a point in the opposite direction. This is repeated until
the algorithm converges. Then, we have found a local minimum—or are at
least are very close to it.
In the vanilla form, the only parameter to know is the base learning rate.
SGD is a more computationally efficient form of Gradient Descent.
SGD only estimates the gradient for the loss from a small sub sample
of data point only, enabling it to run much faster through the
iterations. Theoretically speaking, the loss function is not as well
minimized as with BGD. However, in practice, the close approximation
that you get in SGD for the parameter values can be close enough in many
cases. Also, the stochasticity is a form of regularization, so the
networks usually generalize better.