Build and configure neural network architectures like CNNs and RNNs for ML tasks such as image classification and text generation. Generate PyTorch code with validation and error handling, get metrics and insights, save artifacts, and produce documentation.
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Train and optimize machine learning models with automated workflows
Dynamic/morphogenetic neural networks - grow, prune, adapt topology during training. Covers continual learning, gradient isolation, PEFT/LoRA, modular composition, lifecycle orchestration. 7 reference sheets, 2 commands, 1 SME agent.
AI/ML development: LLM architecture, prompt engineering, ML ops, and NLP with production deployment focus
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
TensorFlow machine learning and deep learning framework skills.
ML engineering plugin: Give your AI coding agent ML engineering superpowers.