A real understanding of machine learning and neural networks requires logical thinking and solid mathematical foundations. Math is the language of all scientific knowledge. Without it, you're limited to superficial use and intuition.
This section covers the minimum mathematical background needed to follow learning paths in this repository.
Understanding vectors, matrices, and their transformations is essential for everything from data representation to backpropagation.
Recommended:
- 3Blue1Brown: Essence of Linear Algebra 3Blue1Brown
A visual explanation of core topics like linear transformations, matrix multiplication, and basis changes.
Key for understanding optimization, loss functions, and how learning actually happens through gradients.
Recommended:
- 3Blue1Brown: Essence of Calculus
Covers derivatives, chain rule, and the intuition behind how functions change.
Required for evaluating models, understanding distributions, and reasoning under uncertainty.
Topics to know:
- Probability basics (independence, conditional probability, Bayes rule)
- Distributions (normal, Bernoulli, binomial)
- Mean, variance, standard deviation
- Sampling, confidence intervals
- Correlation vs. causation
Recommended:
At the very least, watch the video series to refresh key concepts. Use pen and paper as you go, writing things out helps build actual understanding.