3.1 Neural Network Core

Role at Stage 3: Deep Learning

Connect tensors, layers, losses, gradients, and optimizers to learning behavior. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.

Explanation

This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.

Part Diagram

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flowchart LR
  P["<b>3.1</b><br/>Neural Network Core"]
  P --> S1["<b>3.1.1</b><br/>Tensors and Shapes"]
  P --> S2["<b>3.1.2</b><br/>Layers Activations and<br/>Parameters"]
  P --> S3["<b>3.1.3</b><br/>Loss Functions"]
  P --> S4["<b>3.1.4</b><br/>Backpropagation and<br/>Autograd"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts

Sub-part folder	What it explains
3.1.1 Tensors and Shapes	Tensors and Shapes is the working skill inside Neural Network Core that helps you build the stage artifact, A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes, while collecting enough evidence to trust the result.
3.1.2 Layers Activations and Parameters	Layers Activations and Parameters is the working skill inside Neural Network Core that helps you build the stage artifact, A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes, while collecting enough evidence to trust the result.
3.1.3 Loss Functions	Loss Functions is the working skill inside Neural Network Core that helps you build the stage artifact, A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes, while collecting enough evidence to trust the result.
3.1.4 Backpropagation and Autograd	Backpropagation and Autograd is the working skill inside Neural Network Core that helps you build the stage artifact, A PyTorch training project with loops, validation curves, checkpoints, ablations, and debugging notes, while collecting enough evidence to trust the result.

What a Person Who Masters This Part Can Do

• Explain how Neural Network Core supports a pytorch training project with loops, validation curves, checkpoints, ablations, and debugging notes..
• Build and inspect this artifact: Train a tiny neural network and inspect its learning curve.
• Measure progress with: Plot loss, accuracy, gradients, and one failed run.
• Debug at least one failure mode before moving to the next part.

Build and Measure

Build: Train a tiny neural network and inspect its learning curve.

Measure: Plot loss, accuracy, gradients, and one failed run.

Tests

Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.

Open Part Exam

Back to Stage

Return to Stage 3: Deep Learning.