Applying deep learning requires simultaneously understanding
- (i) the motivations for casting a problem in a particular way;
- (ii) the mathematics of a given modeling approach;
- (iii) the optimization algorithms for fitting the models to data; and
- (iv) and the engineering required to train models efficiently, navigating the pitfalls of numerical computing and getting the most out of available hardware.
- Supervised Learning:
- Regression:
A good rule of thumb is that any How much? or How many? problem should suggest regression.
- Classification:
pose a problem as “Is this a _ ?”, then it is likely, classification
- Regression: