Thread by @TivadarDanka

If I had to learn Math for Machine Learning from scratch, this is the roadmap I would follow:

1. Linear Algebra

These are non-negotiables:

• Vectors
• Matrices
• Equations
• Factorizations
• Matrices and graphs
• Linear transformations
• Eigenvalues and eigenvectors

Now you've learned how to represent and transform data.

2. Calculus

Don't skip any of these:

• Series
• Functions
• Sequences
• Integration
• Optimization
• Differentiation
• Limits and continuity

Now you understand the math behind algorithms like gradient descent and get a better feeling of what optimization is.

3. Multivariable Calculus

Here's how you start:

• Multivariable functions
• Derivatives and gradients
• Optimization in multiple variables

In real life, neural networks involve functions with thousands of parameters, and you need to know how they change together.

4. Probability Theory

Learn this:

• Distributions
• Expected values
• Random variables

Now you know how to model uncertainty, learn from data, and make predictions.

If you are looking for a single resource containing all these topics, I have good news for you.

I have packed 20 years of math studies into 700 pages full of intuitive and application-oriented lessons, the ultimate learning resource for you.

Get it now: amazon.com/Mathematics-Ma…