ds-skills

Memory-efficient Keras generator that streams sharded CSV files in chunks, renders strokes to images on-the-fly, and yields batches for training on datasets too large for memory

Stack 1D convolutions for local feature extraction before bidirectional LSTMs to classify variable-length stroke sequences into hundreds of doodle categories

Partition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files

Normalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification

Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order

Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function

Extract per-frame CNN features then classify the temporal sequence with stacked GRU layers and a boolean mask for variable-length video inputs

Average per-frame sigmoid predictions across sampled video frames to produce a stable video-level classification probability

Resize images preserving aspect ratio then zero-pad to a square to avoid distortion artifacts in face crops or object detection inputs

Efficiently sample N evenly-spaced frames from a video using OpenCV grab/retrieve pattern with optional resize for batch face detection or classification

Encode motion and velocity by computing per-channel pixel differences between consecutive frames instead of stacking raw frames for RL visual observations

Encode game state as a Super Mini Map (SMM) with separate binary channels for players, ball, and ownership, bit-packed for efficient transfer in RL training

Predict where a projectile will land using kinematic equations with estimated gravity to intercept aerial passes in game AI simulations

Two-level group-then-pattern dispatch for game AI agents where groups filter by game state and ordered patterns within a group fire the first matching action

Score candidate movement directions by average distance to nearby opponents and pick the safest path for ball-carrying agents in game AI

Predict correlated targets sequentially, using earlier target predictions as input features for subsequent targets to exploit inter-target dependencies

Map calendar dates to categorical season phases (offseason, preseason, regular, postseason) using np.select with boundary date conditions

Predict day-by-day via Kaggle's iter_test API while maintaining a rolling history buffer for computing lag features online

Generate lag features for multiple targets over N days by shifting evaluation dates and self-joining per entity, creating a wide feature matrix of past target values

Use scipy periodogram to identify dominant seasonal frequencies in a time series before selecting Fourier feature orders or ARIMA seasonal parameters

Encode a 2D game board into a normalized multi-channel feature tensor with log-scaled resources, signed unit counts, and directional features for RL agents

Divide a game into repeating phases (attack, mine, spawn) with turn-modular gating so the agent cycles between aggressive and economic behavior

Wrap a Kaggle competitive game environment as an OpenAI Gym env with continuous action space for training PPO agents via stable-baselines3

Encode closed rectangular patrol routes as compact direction-distance strings for fleet pathfinding on toroidal game grids

Shape RL rewards with time-decaying asset weights and time-increasing resource weights so the agent transitions from expansion to accumulation as the game progresses

Manual stable diffusion inference loop with classifier-free guidance that interpolates between unconditional and conditional noise predictions for controllable image generation

Generate descriptive text prompts from images by combining BLIP captioning with CLIP cosine similarity against curated label banks for medium, movement, and flavor attributes

GPU-accelerated k-NN regression on CLIP image embeddings using cosine distance and inverse-distance-power weighting to predict target embedding vectors

Encode text prompts into fixed-length dense vectors using SentenceTransformer for cosine-similarity evaluation in image-to-text retrieval tasks

Ensemble predictions from heterogeneous vision-language models by blending their output embeddings with fixed scalar weights in embedding space

Override model predictions with last known value for low-activity or low-density entities where learned trends are unreliable

Convert a density metric back to integer counts using known population, round to nearest integer, then recompute density to exploit the discrete nature of the target

Walk-forward stacking ensemble that trains base models on expanding windows and a meta-learner on their out-of-fold predictions across time

Transform forecasting target to next/current ratio minus one so that optimizing MAE or squared error implicitly minimizes SMAPE

Walk backward through a time series and multiplicatively rescale segments when jumps exceed a fraction of the running mean to correct data collection anomalies

Load large LLMs with 4-bit NF4 quantization and optional double quantization via BitsAndBytes to reduce GPU memory by 4x while preserving inference quality

Structured prompt template for recovering the instruction that transformed one text into another, with labeled original/rewritten fields and explicit task framing

Replace the actual input text with a generic low-meaning passage to prevent the LLM from fixating on content specifics, forcing it to focus on stylistic and structural transformation cues

Rank candidate prompts by computing LLM perplexity of the full conversation conditioned on each prompt, selecting the lowest-perplexity candidate as the best match

Truncate LLM output to exactly N sentences and fall back to a known-good baseline string when output is empty or too short