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Solve complete questions with progressive hints, worked solutions and Python implementations. Each question explains its assumptions and source match. Original interpretations are labelled explicitly.

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Catalogue titles

Public standard problems and original exercises; hidden Trading Interview questions have not been verified.

174 titles have been investigated but remain unresolved. Their missing details are recorded without adding substitute exercises. Every playable question has a guided animated answer; selected questions also have custom diagrams.

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752 matching entries · page 1 of 16

Machine Learning · Medium

Gradient Descent From Scratch

Original interpretation · Match confidence: medium

Company use unverified
About this question’s source match

The title with subcategory Optimization specifies the method assessed. This is an original worked scenario using the standard method documented in the public reference. The inaccessible Trading Interview statement has not been verified; numerical data, conventions and framing are newly authored rather than claimed source reproductions.

Implement fixed-step gradient descent for J(w)=(w-3)^2/2. Starting at 0 with eta=.5, return w after 3 updates. Derive the recurrence for the error w-3, identify the learning rates giving convergence for all starting points, and explain eta=2 and steps=0.

Assumptions and constraints

  • All data and timing conventions are those explicitly stated in the prompt.
  • Show intermediate calculations; use the specified normalization and boundary conventions.
  • Inputs must be small enough that sums, products and updates remain finite in Python floating-point arithmetic.

Examples

start=0,eta=.5,steps=3
→ 2.625

The error halves on each update.

Company evidence · unverified

No company-specific source has been matched to this exercise. Its references support the topic or solution method; they do not establish employer use.

References

Read the supporting material without leaving your question.

  • d2l.ai

    Dive into Deep Learning: gd

    Standard method and mathematical definitions; not the hidden catalogue question or the newly authored numerical data.

Catalogue metadata captured 2026-10-12. Metadata source.