Deep Learning Implementation and Deployment

This unit advances foundational deep learning concepts into practical implementation, optimization, and application development using frameworks such as TensorFlow, Chainer, and Torch. Students will focus on model optimization, probability calibration, robustness (including adversarial defenses), performance tuning, and deploying real-time, user-facing deep learning solutions.

Learning Objectives

• Analyze training and validation behavior of deep learning models using optimization concepts (loss landscapes, gradients, learning rate schedules) and report findings with quantitative metrics (loss curves, accuracy, convergence rate).
• Apply probabilistic methods and regularization techniques (e.g., softmax calibration, dropout, weight decay, data augmentation) to convert model outputs to calibrated probabilities and reduce overfitting, as demonstrated by improvements in validation metrics and reduced train/validation performance gap.
• Evaluate model robustness and performance by designing and executing experiments to detect edges/shapes, measure latency and throughput, and assess vulnerability to adversarial attacks; propose and test mitigation strategies and document results with comparative metrics.
• Demonstrate the ability to develop and deploy a prototype deep learning application (real-time behavior analysis, sentiment analysis, or translation) using an appropriate framework (TensorFlow, Chainer, or Torch), integrating device considerations, dynamic graph computation, and basic user/support workflows while communicating design trade-offs.