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About this interview
A technical interview on Deep Learning and Neural Networks, pitched at the hard level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain forward and backward propagation, activation functions, and gradient descent variants
Diagnose and resolve vanishing/exploding gradients, overfitting, and slow convergence
Describe CNN and RNN architectures and articulate their inductive biases for images and sequences
Summarize the transformer self-attention mechanism and contrast it with RNNs for sequence tasks
Topics covered
Forward propagationActivation functionsBackpropagationGradient descent variantsOptimizersTraining dynamicsWeight initializationOverfitting diagnosisRegularizationNormalizationVanishing gradientsExploding gradientsTraining diagnosticsTransfer learning
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you walk me through what happens during a single forward pass of a feedforward neural network — from input to output?
“How do L1 and L2 regularization differ in their effect on learned weights, and which tends to produce sparse models and why?
“What is the core intuition behind self-attention in a transformer? How does it differ from how an RNN processes context?