Deep Learning Modeling and Optimization

Description

Provide deep learning model design, training optimization, problem diagnosis, and engineering practice guidance to ensure models meet requirements for accuracy, efficiency, and deployability.

When to Use

• User requests "design a deep learning model" or "how to train XX task"
• User asks about training issues (overfitting, underfitting, gradient vanishing/explosion, NaN, slow convergence)
• User needs paper reproduction or cutting-edge technology analysis (Transformer, Diffusion, ViT, etc.)
• User seeks framework usage recommendations (PyTorch, TensorFlow, JAX) or code implementation
• User asks about best practices in CV, NLP, reinforcement learning domains
• User needs model compression, quantization, deployment, or inference optimization solutions
• User requests data processing, data augmentation, or sampling strategy recommendations
• User compares different model architectures or training strategies