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Why is optimization for deep learning models so difficult?
There are plenty of challenges in deep learning optimization but most of them are related to the nature of the gradient of the model. The local minima problem arises when the gradient encounters many local minimums that are different and not correlated to a global minimum for the cost function.
What is the difference between an optimization problem and a machine learning problem?
Optimization lies at the heart of machine learning. Most machine learning problems reduce to optimization problems. On the other hand, mathematical programming algorithms equip machine learning researchers with tools for training large families of models.
What is the difference between optimization and deep learning?
We have some metric, like accuracy, or even better precision/recall or F1 score, we have a model with learnable parameters (our network) and we have our data (the training and test sets).
What’s the difference between optimization and machine learning?
Simply put, in optimization problems, we are interested in some metric P and we want to find a function (or parameters of a function) that maximizes (or minimizes) this metric on some data (or distribution) D. This sounds just like Machine (or Deep) Learning.
What is the problem with deep neural networks?
A difficulty that we are faced with when training deep Neural Networks is that of vanishing or exploding gradients. For a long period of time, this obstacle was a major barrier for training large networks.
When to use Newton’s algorithm in deep learning?
However if the gradient is getting less steeper, we might be heading towards a region at the bottom of pathological curvature. Here, Newton’s algorithm gives us a revised learning step, which is, as you can see is inversely proportional to the curvature, or how quickly the surface is getting less steeper.