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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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RELATED CONCEPTS
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.→
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