“PyTorch and TensorFlow”

AI and machine learning development with PyTorch, TensorFlow, and LLM integration.

Specialist subagent: Expert machine learning engineer for PyTorch, TensorFlow, LLM integration, and ML pipelines

Train ML models with scikit-learn, PyTorch, TensorFlow.

Linux data science and AI/ML environment setup - TensorFlow, PyTorch, CUDA, Jupyter

TensorFlow 2.x / Keras patterns and solutions: model building, training loops, classification, regression, NLP, inference serving, Databricks deep learning.

Routes CS230 code-example requests to the correct PyTorch or TensorFlow vision and NLP workflows for SIGNS image classification and named-entity recognition.

Scalable data processing for ML workloads.

World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems.

Guides CUDA-based BigGAN-PyTorch training, checkpoint sampling, dataset preparation, metric preparation, model customization, and optional TensorFlow Hub…

Model interpretability and explainability using SHAP (SHapley Additive exPlanations).

Route Auto-PyTorch tabular and forecasting workflows.

Create efficient data pipelines with tf.data

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Structured error knowledge database for AI coding agents.