## How to solve coding problems

Practical problem solving strategies for the working data scientist

Practical problem solving strategies for the working data scientist

A measure-theoretic introduction

A brief guide about how to minimize a function with millions of variables

A look beyond function fitting

Looking behind the curtain of one of the most influential dimensionality reduction algorithms

Understanding the inner workings of neural networks from the ground-up

How to update our models given new observations

Looking behind one of the most commonly used loss functions

The universal approximation theorem

An in-depth explanation of principal component analysis

The building blocks of describing and manipulating data

What is behind the not-so-simple formula?

A simple explanation