Sign in to track progress, unlock prep layers, and browse your pipeline.Sign in
Explain core ML and DL fundamentals
ML/DL Concept Questions (Take-home)
This take-home assignment requires you to provide concise and correct answers to a series of fundamental Machine Learning and Deep Learning concept questions.
1. Principal Component Analysis (PCA)
- What do the eigenvectors of the covariance matrix represent?
- How do they relate to principal components and explained variance?
2. Decision Trees
- Define Gini impurity.
- Show how to compute it for a node.
- Explain how it is used to choose splits.
3. Reinforcement Learning
- Write the Bellman optimality equation (for V* or Q*).
- Explain its role in policy evaluation and improvement.
4. Regularization (Dropout)
- What is dropout?
- How does it behave at training vs. inference time?
- Why does it act as a regularizer?
5. Training Stability
- What is gradient clipping and when is it useful?
- How do residual connections in ResNets help mitigate vanishing gradients?
6. Optimization Landscape
- Why are deep learning objectives typically non-convex?
- What are the practical implications for optimization (e.g., local minima vs. saddle points)?
7. Transformers
- Describe scaled dot-product attention.
- Explain why the dot products are scaled by 1/sqrt(d_k).
AI interview prep
Core dilemma
Balancing the breadth of foundational ML/DL knowledge with the depth of understanding practical implications and interdependencies of core algorithms and techniques.
Mid-level signals3
Senior signals3
Red flags3
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Explain core ML and DL fundamentals
ML/DL Concept Questions (Take-home)
This take-home assignment requires you to provide concise and correct answers to a series of fundamental Machine Learning and Deep Learning concept questions.
1. Principal Component Analysis (PCA)
- What do the eigenvectors of the covariance matrix represent?
- How do they relate to principal components and explained variance?
2. Decision Trees
- Define Gini impurity.
- Show how to compute it for a node.
- Explain how it is used to choose splits.
3. Reinforcement Learning
- Write the Bellman optimality equation (for V* or Q*).
- Explain its role in policy evaluation and improvement.
4. Regularization (Dropout)
- What is dropout?
- How does it behave at training vs. inference time?
- Why does it act as a regularizer?
5. Training Stability
- What is gradient clipping and when is it useful?
- How do residual connections in ResNets help mitigate vanishing gradients?
6. Optimization Landscape
- Why are deep learning objectives typically non-convex?
- What are the practical implications for optimization (e.g., local minima vs. saddle points)?
7. Transformers
- Describe scaled dot-product attention.
- Explain why the dot products are scaled by 1/sqrt(d_k).