Core

Implementing ML From Scratch (NumPy Patterns)

ML-from-scratch coding rounds test whether you can express a model as vectorized array operations rather than Python loops, structure a clean forward and backward pass, and write a numerically careful softmax and cross-entropy. Interviewers watch for the vectorization mindset, correct broadcasting, and whether you stabilize the math before they have to ask. The skill is turning the math on the whiteboard into a few NumPy lines that would actually run on a batch.

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COURSES COVERING THIS TOPIC

No lesson covers this one directly yet. These teach the surrounding topic from the beginning.

Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.

RELATED CONCEPTS

Numerical Stability in Code The Big-O That Actually Matters Backpropagation, Intuitively

PRACTICE THIS IN REAL QUESTIONS

Coding & DSAImplement a numerically stable softmax and cross-entropy loss from scratch.→Coding & DSAImplement logistic regression from scratch in NumPy: forward pass, loss, and gradient descent.→Coding & DSAImplement a vanilla RNN cell from scratch: forward over a sequence and backprop through time.→Coding & DSAImplement k-means from scratch, including k-means++ initialization and a convergence check.→Coding & DSAImplement multi-head self-attention from scratch in NumPy, with a causal mask.→Coding & DSAGiven a query vector and N stored vectors, return the top-k most similar by cosine similarity, efficiently.→

COMPANIES THAT ASSUME THIS

Anthropic Google Cognition Palantir Meta Sierra

NEXT IN CODING & ENGINEERING CRAFTNumerical Stability in Code→