## Machine learning theory made easy.

So, you want to master machine learning. Even though you have experience in the field, sometimes you still feel that something is missing. A look behind the curtain.

Have you ever felt the learning curve to be so sharp that it was too difficult even to start? The theory was so dry and seemingly irrelevant that you were unable to go beyond the basics?

If so, I am building something for you. I am working to create the best resource to study the mathematics of machine learning out there.

Join the early access and be a part of the journey!

### 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 never be in the dark when things go wrong.

### Be a part of the process.

This book is being written in public. With early access, you’ll get each chapter as I finish, with a personal hotline to me. Is something not appropriately explained? Is a concept not motivated with applications? Let me know, and I’ll get right on it!

## The roadmap

## This is what is 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

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