Build and Train Deep Learning Models using TensorFlow/Keras and PyTorch

Learning Objectives

At the end of this theory note, trainees must be able to:

1. Construct sequential and functional neural network models in Keras and PyTorch.
2. Load, clean, and preprocess datasets using tf.data pipelines and PyTorch DataLoaders.
3. Implement a training loop with appropriate loss functions, optimizers, and metrics.
4. Evaluate model performance using validation sets and interpret the results.
5. Apply dropout, early stopping, and hyperparameter tuning to improve model accuracy.
6. Save, load, and deploy a trained model’s weights and architecture.