Explain core ML concepts

Q: Explain core ML concepts

This question evaluates mastery of foundational machine learning concepts — including linear algebra for PCA (eigenvectors/eigenvalues), impurity measures for decision trees, Bellman equations in reinforcement learning, dropout and other regularization techniques, training stability and optimization landscapes, and attention mechanisms and scaling in transformers — within the Machine Learning domain. It is commonly asked to assess theoretical depth and the ability to connect mathematical formulations to practical model behavior, testing a mix of conceptual understanding and practical application expected of a machine-learning engineer.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

ML Theory Check: PCA, Trees, RL, Regularization, Optimization, and Transformers

Context: Provide concise, technically correct explanations suitable for a machine-learning engineer take-home. Use formulas and brief examples where helpful.

Tasks

PCA: Interpret the eigenvectors (and eigenvalues) of the covariance matrix.
Decision trees: Define Gini impurity and explain how it is used to choose splits.
Reinforcement learning: Write the Bellman equation(s) and explain their role.
Neural networks: Explain dropout as a regularization technique.
Training stability: Describe gradient clipping, the vanishing-gradient problem, and how ResNets help.
Optimization landscape: Why are many deep-learning loss surfaces non-convex?
Transformers: Explain attention mechanisms and what “scaling” means in this context (both the scaled dot product and computational scaling with sequence length).

Explain core ML concepts

ML Theory Check: PCA, Trees, RL, Regularization, Optimization, and Transformers

Tasks

Solution

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