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Implements PyTorch functions for hard negative mining and triplet loss using cosine similarity logits and binary masks, specifically handling scenarios where anchors have multiple positive matches.
Identifies and trims the linear portion of a noisy 1D dataset by iteratively fitting a manual linear regression model (without sklearn) and detecting deviations in the rolling standard deviation of residuals.
Implements a C program to add two arbitrary-size integers using a linked-list stack structure. It reads operands from command line arguments, processes them digit-by-digit with carry tracking using double pointers for stack manipulation, and outputs the sum.
Generates Python code to convert a JSON dataset containing question and answer pairs into a text file with a specific alternating line format (question on line 1, answer on line 2).
Create a memory-efficient PyTorch optimizer fusing SM3 and Adalite techniques. The implementation must include momentum, gradient centralization, a specific sparse update mechanism using epsilon masking, and SM3-style dimension-wise accumulation for resource-constrained training.
Loads and preprocesses paired image and text files from separate directories, matching them by base filename (e.g., screen_13.png with html_13.html) for machine learning training.
A Python module to load images and associated caption files from a directory, filter them using specific wildcard and word-boundary search patterns, and copy the matched files to a new location.
Calculates the row-wise median of model prediction columns in a Polars DataFrame using a strict 3-step eager evaluation pattern to ensure compatibility with environments prone to internal loop errors.
Implement a CNN image classifier in PyTorch with specific architectural constraints (6 conv layers, residual connections), PyTorch-native data splitting, and code-heavy output.
Implements a Python-based query executor that converts informal keywords to SQL commands, parses queries preserving string literals, and executes operations on pandas DataFrames.
Managing prompt iterations, testing changes, and tracking what works.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD,...
Comprehensive deep learning guidelines for neural network development, training, and optimization.
Structured and unstructured model pruning: weight pruning, attention head pruning, layer removal, and neural architecture search for efficient LLMs.
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias ...
Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Expert guidance for DeepEval, the open-source framework for unit testing LLM applications. Helps developers write test cases, define custom metrics, and integrate LLM quality checks into CI/CD pipelines using a pytest-like interface.
Property-based testing (PBT) patterns with fast-check (JS/TS), Hypothesis (Python), and gopter (Go). Generate random inputs, define invariants, shrink failures to minimal cases. Adapted from Trail of Bits. Use when testing pure functions, parsers, serializers, state machines, or any code where ex...
PyTorch deep learning development with transformers, diffusion models, and GPU optimization.
Expert guidance for working with Hugging Face Transformers library for NLP, computer vision, and multimodal AI tasks.
基于协议的依赖注入，用于可测试的Swift代码——使用聚焦协议和Swift Testing模拟文件系统、网络和外部API。
Use shell commands or Python libraries to extract PDF text when read_file PDF handler fails
Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.
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