Learn 80% about what they are and their applications with 20% effort

A breakdown of the three fundamental math fields required for machine learning: statistics, linear algebra, and calculus.

These common, basic statistics and machine learning concepts are crucial for landing a data scientist role

We think basis-free, we write basis-free, but when the chips are down we close the office door and compute with matrices like fury.

We roll up our sleeves and start to deal with matrices

There is an extremely naive model of science and innovation called the linear model: The model postulated that innovation starts with basic research, is followed by applied research and development,…

A useful tool in your quiver

The physical meaning of multiplying two matrices and how it works on several special matrices.

Visualizing matrix transposition, to make sense of transpose-related formulas. The post Understanding Matrices | Part 3: Matrix Transpose appeared first on Towards Data Science. Generated by RSStT.…

A walk-through of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training. We use it to predict the severity of an infection on a scale based on information on just rough outcomes…