Advanced Techniques and Deployment

As models grow deeper and more specialized, preventing overfitting and leveraging pre-trained networks become critical. This section addresses best practices for regularization, transfer learning, and advanced topics like GANs or transformers. It also covers real-world deployment considerations, from model compression to containerisation.

REGULARIZATION, TRANSFER LEARNING, AND ADVANCED TOPICS

Learning Objectives

• Prevent overfitting with dropout, batch normalization, early stopping

• Fine-tune pre-trained networks (ResNet, BERT) for specialized tasks

• Explore GANs, transformers, and deployment best practices

Indicative Content

• Regularization

  • L1/L2, dropout, batch normalization, data augmentation

• Transfer Learning

  • Freezing layers, partial training

• GANs & Transformers

  • Generative and attention-based concepts

• Deployment & Optimization

  • Quantization, pruning, containerization

TOOLS & METHODOLOGIES (ADVANCED TECHNIQUES & DEPLOYMENT)

• Framework Capabilities

  • Transfer learning APIs (pre-trained models)

  • Advanced architectures (GAN, transformer blocks)

• Regularization Strategies

  • Data augmentation, early stopping, batch normalization

• Deployment

  • Containerized environments, hardware optimization (pruning, quantization)

• Monitoring

  • Model drift detection, continuous integration for retraining