Book Image

The Statistics and Calculus with Python Workshop

By : Peter Farrell, Alvaro Fuentes, Ajinkya Sudhir Kolhe, Quan Nguyen, Alexander Joseph Sarver, Marios Tsatsos
Book Image

The Statistics and Calculus with Python Workshop

By: Peter Farrell, Alvaro Fuentes, Ajinkya Sudhir Kolhe, Quan Nguyen, Alexander Joseph Sarver, Marios Tsatsos

Overview of this book

Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python. The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions. By the end of this book, you’ll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.
Table of Contents (14 chapters)
Preface

Introduction

In the previous chapter, we learned how to calculate derivatives and integrals. Now, we're going to use those tools to find the lengths of curves and spirals and extend this reasoning to three dimensions to find the area of a complicated surface. We'll also look at a common tool that's used in calculus, the infinite series, which is used to calculate important constants and approximate complicated functions. Finally, we'll look at an important idea in machine learning: finding the minimum point on a curve. When you use a neural network, you create a kind of "error function" and work hard to find the point on the surface that gives the minimum error. We'll create our own kind of gradient descent function to keep traveling downward until we're at the bottom of the surface.