Practical guides to learning AI and machine learning, written by Gaurav Jain. Worked examples, projects with complete code and learning plans you can follow.
Compare full and rank-one parameter updates on the same synthetic regression task, with checked parameter counts and held-out reconstruction error.
comparison4 min read
Trace a 2×3 batch through a 3×2 matrix, calculate individual entries and distinguish matrix multiplication from broadcasting and elementwise products.
tutorial4 min read
Trace a two-layer ReLU network, derive one gradient with the chain rule and compare it with PyTorch autograd.
tutorial4 min read
Predict broadcast shapes from the right, normalize columns correctly and diagnose an accidental outer operation with checked arrays.
tutorial4 min read
Trace shared memory through slicing, advanced indexing and assignment, and set an explicit copy boundary in a numerical workflow.
troubleshooting4 min read
Calculate a two-sided binomial p-value, define what counts as extreme, and separate evidence against a model from effect size and practical importance.
explanation4 min read
Compute group totals and row-level shares from an explicit table, including missing keys and a zero-total group.
tutorial4 min read
loc selects pandas rows and columns by label; iloc selects by integer position. See a side-by-side table, slicing rules, errors and examples you can run.
tutorial8 min read
Calculate a join's row count, reproduce many-to-many expansion, and use validate and indicator to enforce the intended table relationship.
troubleshooting4 min read
The courses take each idea from the intuition and the maths to code you write yourself, from your first line of Python to machine learning.