Unit ID: DLPY-M01-U00 Estimated active time: 15-20 minutes
Let us begin
You already know the basic machine learning habit:
> Define the problem, prepare data, train a model, evaluate it honestly, inspect errors, and state limits.
Deep learning does not remove that habit. It makes the habit more important.
In this course, we will use PyTorch to see the mechanics directly. You will work with tensors, gradients, loss, model layers, training loops, and model artifacts. You will not only call a high-level function and hope it works.
What this course is
This is the PyTorch mechanics course.
That means we will focus on:
- tensors and shapes;
- autograd and gradients;
- training loops;
- neural network modules;
- learning curves;
- image and text representation examples;
- transfer learning boundaries; and
- honest model documentation.
PyTorch is useful here because it lets us write the training process clearly. You can see when the model predicts, when loss is calculated, when gradients are computed, and when the optimizer updates the parameters.
What this course is not
This is not a course on training large language models from scratch. It is not a cloud GPU engineering course. It is not a production deployment course. It is not the TensorFlow/Keras course.
Those boundaries are important. A course that tries to teach everything at once usually teaches little well.
Our goal is smaller and stronger:
> Build deep learning understanding you can inspect.
The first promise
We will keep the first release CPU-first. You should be able to learn the core ideas without renting a GPU.
Some later optional examples may mention GPU use or pretrained models. They will be clearly marked. They will not be required for the core path.
The second promise
Every substantial model must have evidence.
Evidence can include:
- a baseline;
- training and validation curves;
- an error table;
- a dataset card;
- a model card; and
- a limitation note.
A high score without a limit note is not enough. A beautiful neural network diagram is not enough. A pretrained model is not automatically safe or useful.
Pause and decide
Read this task:
> We have 120 rows of tabular data. We need to predict whether a learner may need support next week. The result may affect who gets attention from staff.
Should deep learning be the first choice?
Probably not. The data is small. The consequence affects people. A simpler baseline, careful features, human review, and a clear support policy should come first.
That does not mean deep learning is bad. It means the tool must fit the task.
What you will do in this module
You will decide when deep learning is worth trying. You will set the runtime boundary. You will prepare your evidence templates. Then you will take a readiness diagnostic before starting tensors in Module 2.