TensorFlow Model Training and Deployment

This unit covers building, training, visualizing, and deploying TensorFlow models. Students will learn tensor transformations, logistic regression and deep learning techniques, training with backpropagation, model regularization and tuning, and the use of TensorBoard, Estimators, and Google Cloud tools.

Learning Objectives

• Analyze and transform tensors to prepare inputs and perform image operations, creating robust input functions for model training
• Implement binary and multinomial logistic regression and construct deep neural network architectures using TensorFlow operations, variables, layers, and dropout regularization
• Demonstrate use of training workflows and tooling by running Estimators, visualizing training with TensorBoard, and managing jobs and artifacts with gcloud and Google Cloud Storage
• Evaluate model performance and apply hyperparameter tuning, backpropagation diagnostics, and regularization techniques to improve accuracy and generalization