Skip to content
tezvyn:

All bites

The whole library, newest first. Filter by what you are here for, or pick a topic if you already know.

8668 bites

Page 57

Content & Copywriting2 min read

Programmatically create an ad via a marketing API

Authenticate, create a campaign with an objective, an ad set with budget and targeting, a creative with the copy, then the ad linking them.

Content & Copywriting2 min read

Design a programmatic SEO landing-page system

Structured data plus templates, genuinely unique per-page data, static build-time generation, quality gates and indexation control.

Content & Copywriting1 min read

Explain TF-IDF and its use in SEO analysis

Term frequency times inverse document frequency surfaces distinctive terms; comparing a draft to top-ranking pages reveals missing topical terms.

Content & Copywriting2 min read

Data model for an editorial calendar

A content entity with publish_date and a status enum, a foreign key to authors, indexes on date and status.

Content & Copywriting1 min read

Design a next-best-article recommender

Content-based on article embeddings, collaborative on behavior, a hybrid blend, popularity or onboarding fallbacks for new users.

Content & Copywriting1 min read

Structuring release notes for breaking API changes

Lead with a summary and why, group breaking changes with before/after migration steps, give deprecation timelines and an upgrade path.

Content & Copywriting1 min read

TF-IDF Analysis

TF-IDF weights a term by how often it appears in a document against how rare it is across the corpus, surfacing words distinctive to that document rather than ones common everywhere, which powers keyword extraction and search ranking.

Computer Vision1 min read

Design an active learning loop for detection

Seed-train, score the pool by uncertainty plus diversity, batch to annotators, retrain, repeat.

Computer Vision2 min read

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.

Computer Vision1 min read

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.

Computer Vision1 min read

Design real-time multi-object tracking for AV

Fast detector, Kalman motion model, Hungarian association on IoU plus appearance, track lifecycle for occlusions.

Computer Vision1 min read

Loss functions for imbalanced medical segmentation

Cross-entropy is swamped by background; Dice optimizes overlap directly; Focal down-weights easy pixels.

Computer Vision1 min read

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.

Computer Vision1 min read

Homography vs fundamental matrix degeneracy

Homography fits planar scenes or pure rotation; fitting a fundamental matrix there is degenerate because points lack depth variation.

Computer Vision2 min read

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.

Computer Vision2 min read

Why averaging shots reduces image noise

Random noise averages out while signal stays, so SNR rises with the square root of frame count; limits include motion and fixed-pattern noise.

Computer Vision2 min read

The role of anchor boxes in detectors

Anchors are reference boxes of set scales and ratios; the model regresses offsets, framing detection as classification plus refinement; downsides are tuning, imbalance.

Computer Vision2 min read

Triangulating 3D points from two views

Back-project each 2D point into a ray, intersect rays from both cameras using known poses, solve via DLT and refine by minimizing reprojection error.

Computer Vision2 min read

Single vs multi-image super-resolution

SISR hallucinates detail from one image, multi-image fuses sub-pixel shifts; pixel losses blur, perceptual or GAN losses add realism.

Computer Vision1 min read

Non-maximum suppression in detection

Detectors emit many overlapping boxes per object; NMS keeps the highest-scoring box and removes others above an IoU threshold.