Intermediate everything in AI & ML, page 6
Explain k-means user segments to a marketing team
Name each segment, profile its defining traits, show size and value, recommend an action.
Two ways accurate data can still mislead in a chart
Name distortions like truncated axes or cherry-picked ranges, give the fix for each, explain why each misleads.
Reconcile rising sign-ups with falling revenue per user
Reconcile the metrics via total revenue and segment mix, frame the tradeoff, recommend an action.
Spark RDDs, DataFrames, and Datasets
RDDs are low-level typed object collections with no built-in optimization; DataFrames are named columns optimized by Catalyst and Tungsten; Datasets add compile-time type safety in…
Data skew in Spark and salting
Data skew is uneven key distribution sending most rows to one partition and straggler task; salting appends a random suffix to hot keys to spread them across partitions, joining in two…
Spark broadcast join versus shuffle join
A broadcast join sends the small table to every executor so the large table joins locally with no shuffle of its rows; the default sort-merge join shuffles both tables across the network, which is costly.
RL components and how Q-learning works
Agent acts on the environment, observes state and reward, seeking to maximize cumulative discounted reward; Q-learning iteratively updates Q(s,a) toward reward plus discounted best…
Analyzing skewed revenue-per-user experiments
Heavy tails inflate variance and slow significance, and the mean is dominated by whales; mitigate via winsorization or capping, log transforms, CUPED variance reduction, or bootstrap and rank tests.
Random Forest versus Gradient Boosting
Random Forest trains deep trees in parallel and averages to cut variance; boosting builds shallow trees sequentially, each correcting prior errors to cut bias, often higher accuracy but…
How gradient descent and the learning rate work
Gradient descent steps downhill along the negative gradient to minimize cost; the learning rate sets step size; too high diverges or oscillates, too low converges painfully slowly.
Python Virtual Environments
A virtual environment is an isolated Python installation with its own packages, so each project gets the exact dependency versions it needs without conflicting with other projects or the system Python.
Generative Adversarial Network (GAN)
A GAN trains two networks in competition: a generator that fabricates fake samples and a discriminator that judges real versus fake. Their adversarial game pushes the generator toward realistic outputs, enabling image synthesis and data generation without…
Design an active learning loop for detection
Seed-train, score the pool by uncertainty plus diversity, batch to annotators, retrain, repeat.
What does N-way K-shot classification mean?
N is classes per episode, K is labeled examples per class in the support set, prediction is on a separate query set.
Filter-based vs optimization-based SLAM
EKF folds past poses into one Gaussian; optimization keeps a sparse graph and re-linearizes; the latter wins on accuracy and loop closure.
Design real-time multi-object tracking for AV
Fast detector, Kalman motion model, Hungarian association on IoU plus appearance, track lifecycle for occlusions.
Loss functions for imbalanced medical segmentation
Cross-entropy is swamped by background; Dice optimizes overlap directly; Focal down-weights easy pixels.
How is IoU computed and why prefer mIoU?
IoU is intersection over union of predicted and true pixels; mIoU averages per class; pixel accuracy is dominated by background.
Homography vs fundamental matrix degeneracy
Homography fits planar scenes or pure rotation; fitting a fundamental matrix there is degenerate because points lack depth variation.
PTQ versus QAT for model quantization
PTQ quantizes a trained model with calibration data, fast but may lose accuracy; QAT simulates quantization in training so weights adapt.
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