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 it, 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!
Who is this book for?
Machine learning-first approach to mathematics. Master the art of storing, manipulating, and analyzing data.
Engineers and developers.
All the practical knowledge you need to know, with intuitive explanations you wish you had in school.
Whether it is your first course in mathematics, or refreshing the topic after years: this book is for you.
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.Ryszard ZygałaData scientist, PhDfrom Poland
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.Adam HulmanSenior Data Scientistfrom Denmark
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!Parviz D.Lead Solutions Architectfrom the US
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.Raj ArunLead - Analytics & Data Sciencefrom India
This is what will be covered in detail
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
Function limits and continuity
Minima, maxima, and the derivative
Basics of gradient descent
Partial derivatives and gradients
Minima and maxima in multiple dimensions
Gradient descent in its full form
The mathematical concept of probability
Distributions and densities
Information theory and entropy
Fundamentals of parameter estimation
Maximum likelihood estimation
The Bayesian viewpoint of statistics
Bias and variance
Measuring predictive performance of statistical models
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
Logistic regression, revisited
Loss functions, from a neural network perspective
Stochastic gradient descent
The Lookahead optimizer
The convolutional layer, in-depth
Dropout and BatchNorm
Fundamental tasks of computer vision
Alexnet and Resnet
Generative Adversarial Networks
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Mathematics of Machine Learning early access
- 400+ pages of mathematics, new chapters delivered continuously
- PDF + jupyter-book format
- Python code examples for all concepts
- Lifetime updates
- Single-variable calculus
- Multivariable calculus
- Probability theory and statistics
- Foundations of machine learning
- Internals of neural networks
About the author
Hi there! My name is Tivadar.
I obtained my PhD in 2016 in the field of pure mathematics. As a postdoctoral researcher, I joined a computational biology group, where I fell in love with machine learning.
Understanding mathematics was a cheat code to supercharge my progress.
As I frequently helped my friends and colleagues to master the fundamental concepts of math and machine learning, I found my new passion in teaching.
Since then, education have become my single focus, and my mission is to make quality math education available for developers, engineers, and scientists.
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.
Get early access now!
Safe and secure payment.
Payments are 100% safe and secure, thanks to Gumroad! No credit card information is received and stored at our end. All data is transmitted via encryption.
100% risk-free money back guarantee.
If you don’t like the book, contact me within 60 days, and I’ll give you a full refund. No questions asked.
Any questions? I am here to help!
If you have any questions, shoot me a message on Twitter @TivadarDanka! I am happy to help!