## Understanding math will make you a better engineer.

Imagine a translating device that allows you to engage in fluent conversations with any person in the world, regardless of the language. This is what mathematics can do for you in science and engineering.

So, I am working to create the best book to study the mathematics of machine learning.

Join the early access!

## What the readers say

With great pleasure I can recommend this wonderful book the creation of which I experience every week. You can explore it in pdf, html and jupyter notebooks.

This book is a fantastic resource for those who want to understand the foundations of ML algorithms and are no longer satisfied with considering them as 'black boxes'. BTW with the 'early access' scheme, waiting for the next chapter to be published feels like waiting for your favourite weekly TV show. Nice.

Very impressed with what you’ve done with the book. As someone who’s not an expert in math I found your material super helpful. Thank you!

Being part of early access gives the reader the pleasure to travel with the author. I'm enjoying it thoroughly. Even more excited about what awaits 2 years from now as per your roadmap. I'm sure this will be a gold mine for data science enthusiasts and practitioners.

### Math explained, as simple as possible.

Every concept is explained step by step, from elementary to advanced. No fancy tricks and mathematical magic. Intuition and motivation first, technical explanations second.

### Open up the black boxes.

Machine learning is full of mysterious black boxes. Looking inside them allows you to be a master of your field and always understand what is going on.

## The roadmap

## This is what will be covered in detail

### Linear algebra

Vector spaces ✓

Structure of vector spaces: norms and inner products ✓

Linear transformations and their matrices ✓

Eigenvectors and eigenvalues ✓

Solving linear equation systems ✓

Special matrices and their decomposition ✓

### Calculus

Function limits and continuity ✓

Differentiation ✓

Minima, maxima, and the derivative ✓

Basics of gradient descent ✓

Integration ✓

### Multivariable calculus

Partial derivatives and gradients

Minima and maxima in multiple dimensions

Gradient descent in its full form

Constrained optimization

Integration in multiple dimensions

### Probability theory

The mathematical concept of probability

Distributions and densities

Random variables

Conditional probability

Expected value

Information theory and entropy

Multidimensional distributions

### Statistics

Fundamentals of parameter estimation

Maximum likelihood estimation

The Bayesian viewpoint of statistics

Bias and variance

Measuring predictive performance of statistical models

Multivariate methods

### Machine learning

The taxonomy of machine learning tasks

Linear and logistic regression

Fundamentals of clustering

Principal Component Analysis

Most common loss functions and what’s behind them

Regularization of machine learning models

t-distributed stochastic neighbor embedding

### Neural networks

Logistic regression, revisited

Activation functions

Computational graphs

Backpropagation

Loss functions, from a neural network perspective

Weight initialization

### Advanced optimization

Stochastic gradient descent

Adaptive methods

Accelerated schemes

The Lookahead optimizer

Ranger

### Convolutional networks

The convolutional layer, in-depth

Dropout and BatchNorm

Fundamental tasks of computer vision

Alexnet and Resnet

Autoencoders

Generative Adversarial Networks

## Want to find out more?

Listen to Practical AI’s interview with Tivadar about the book!

Practical AI 152: The mathematics of machine learning – Listen on Changelog.com

Help me write the book you want.

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