Maths for deep learning!

- Linear Algebra.
- Calculus.
- Statistics.


If you just want to use the existing models, you don't need much maths. But, if you want to build your models, you need some understanding of the terminology.

At the level of data cleaning, we need some maths.
Like Min Max Scaling.

This makes all the data in the range 0-1.
Formula for it:
(X-Xmin)/(Xmax-Xmin)


When we have to give this data to the TensorFlow, we will be dealing with the following mainly.

1) Scalar.  Constant
2) Vector.  1 D
3) Matrix.  2 D
4) Tensor.  N D

So, overall we use maths in the following cases:

1) Normalising.
2) Learning Hyperparameters
3) Initialising Weights.
4) Forward Propagation.
5) Calculate error.
6) Backpropagate to get cost corrected.


Hyper parameters.

1) Batch size.
2) Initial Learning rate.
3) Learning rate schedule
4) Rotations.
5) No. of iterations.
6) Weight decay
7) Random Minor.
8) Transformations.

These parameters can be chosen based on the domain knowledge or we can also use Search approach where we just define a range for each parameter.

Q:If you can do matrix operations using Python, will you still use NumPy?
A:YES. NumPy library is written in C, which makes it faster than native python. 


Python lists can have different data types as well. But, numpy can only have one specific data type.