Frequently Asked Questions
What will I learn in the ML & Deep Learning track?
You’ll learn to design, train, evaluate, and deploy deep learning models using proven architectural patterns. The track balances foundational machine learning principles with cutting-edge deep learning advances like transformers and transfer learning.
How are deep neural networks designed and trained?
You’ll explore how to select architectures, choose loss functions, and tune hyperparameters. Topics include optimization strategies like Adam, batch normalization, and regularization for effective model convergence
What is supervised learning and why does generalization matter?
Supervised learning uses labeled datasets to train models. You’ll learn how to prevent overfitting, evaluate performance, and ensure your models generalize to unseen data using methods like cross-validation and dropout.
How does explainable AI (XAI) support model trust?
XAI techniques—like SHAP, LIME, and attention visualization—help developers interpret model decisions, improve transparency, and meet compliance requirements.
What role do transformers and LLMs play in deep learning?
You’ll examine the architecture behind transformer-based models, understand how pre-training works, and apply transfer learning to build domain-adapted models with fewer resources.