Interview Questions

What you’ll learn:

• Neural network architectures and layers
• Backpropagation and gradient descent
• Regularization and optimization techniques

What you’ll learn:

• Tokenization and text preprocessing
• Transformer architecture and attention
• Language model fine-tuning

What you’ll learn:

• CNN architectures and operations
• Object detection and segmentation
• Image classification techniques

What you’ll learn:

• Markov decision processes
• Policy gradient methods
• Q-learning and value functions

What you’ll learn:

• Bias-variance tradeoff
• Cross-validation and model selection
• Ensemble methods and boosting

What you’ll learn:

• Diffusion models and GANs
• Prompt engineering techniques
• RLHF and alignment

What you’ll learn:

• Hypothesis testing and p-values
• Bayesian vs frequentist approaches
• Probability distributions

What you’ll learn:

• Autograd and computational graphs
• Custom datasets and dataloaders
• Model training and deployment