Train and evaluate deep learning models: Review

The first unit on Deep Neural Network Concepts hits you with a firehose of terms: convolutional layers, max-pooling, transfer learning, ResNet. There’s no analogy, no business case, just a dictionary definition followed by a link to a research paper. I felt like I was reading an internal engineering doc that got accidentally published. My goal was simple: understand what my data science team is talking about when they say a model is 'too slow to train' or why 'transfer learning' saves them time. I left with more questions than I started with.

The whole module feels like it's written for a data scientist who's an expert in PyTorch but has never seen the Azure Machine Learning studio. It's showing them which buttons to click. That's it. For a product manager, this is useless. It doesn't explain *why* you'd choose a CNN for images, what a 'residual connection' actually *solves* in a business context, or how I should think about the trade-offs. The lab is just following instructions; you don't make any decisions. It's the definition of a black box. I can now say I've 'trained a deep learning model in Azure,' but I couldn't explain to anyone what I did or why it mattered.