ml-engineer
0.00
Build production ML systems with PyTorch 2.x, TensorFlow, and
2525 results
(21.6ms)
page 27 / 127
ml-engineer
0.00
Build production ML systems with PyTorch 2.x, TensorFlow, and
ml-engineer
0.00
Build production ML systems with PyTorch 2.x, TensorFlow, and
ml-engineer
0.00
Build production ML systems with PyTorch 2.x, TensorFlow, and
tailwind_mastery
0.00
Tailwind CSS v4, design tokens, responsive patterns and utility-first CSS best practices.
tailwind_mastery
0.00
Tailwind CSS v4, design tokens, responsive patterns ve utility-first CSS best practices.
gemini-embeddings
0.00
Generate text embeddings using Gemini Embedding API via scripts/. Use for creating vector representations of text, semantic search, similarity matching, clustering, and RAG applications. Triggers...
This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON...
Use when teaching complex concepts (technical, scientific, philosophical), helping learners discover insights through guided questioning rather than direct explanation, correcting misconceptions...
Use when you have implemented an equivariant model and need to verify it correctly respects the intended symmetries. Invoke when user mentions testing model equivariance, debugging symmetry bugs,...
langchain_patterns
0.00
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use...
agent_orchestration
0.00
Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete,...
langchain_patterns
0.00
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use...
agent_orchestration
0.00
Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete,...
Build NLP applications using transformers library, BERT, GPT, text classification, named entity recognition, and sentiment analysis
Build collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement
rag_architecture
0.00
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM...
rag_architecture
0.00
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM...
rag_implementation
0.00
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or...
pyvene-interventions
0.00
Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange...