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AI and machine learning · 30 lessons · up to 10 minutes each
PyTorch
A planned path from tensor operations to repeatable model training and inference.
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PyTorch
Permanent access to the lessons published in this track when you buy. Future lessons are not included. 0 of 30 lessons are published; 0 are in owner review and 30 are Coming Soon.
$29.0030 lessons still in progress
Beginner
Lessons 1–10
01 PyTorch: Sketch tensor shapes and dtypes
Topic: tensor shapes and dtypes
Draw a small map of tensor shapes and dtypes in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Beginner4 minComing Soon02 PyTorch: Name the parts of tensor operations and devices
Topic: tensor operations and devices
Label the main pieces in tensor operations and devices in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Beginner5 minComing Soon03 PyTorch: Trace an example of dataset and dataloader
Topic: dataset and dataloader
Follow one input through dataset and dataloader in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Beginner6 minComing Soon04 PyTorch: Compare choices for module and parameter registration
Topic: module and parameter registration
Write down one practical difference between module and parameter registration in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Beginner7 minComing Soon05 PyTorch: Find the boundary in forward pass and loss
Topic: forward pass and loss
Mark where forward pass and loss in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Beginner8 minComing Soon06 PyTorch: Inspect an example of autograd and gradients
Topic: autograd and gradients
Annotate a minimal example of autograd and gradients in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Beginner9 minComing Soon07 PyTorch: Connect optimizer step loop to a neighboring concept
Topic: optimizer step loop
Show how optimizer step loop in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Beginner10 minComing Soon08 PyTorch: Spot a failure mode in evaluation mode and inference
Topic: evaluation mode and inference
Describe one likely failure for evaluation mode and inference in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Beginner3 minComing Soon09 PyTorch: Read an official example of checkpoint save and restore
Topic: checkpoint save and restore
Summarize an official example of checkpoint save and restore in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Beginner5 minComing Soon10 PyTorch: Choose a use case for reproducible training checks
Topic: reproducible training checks
Match one small use case to reproducible training checks in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Beginner6 minComing Soon
Intermediate
Lessons 11–20
11 PyTorch: Build an example with tensor shapes and dtypes
Topic: tensor shapes and dtypes
Create a small working example that uses tensor shapes and dtypes in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Intermediate4 minComing Soon12 PyTorch: Compose tensor operations and devices with another component
Topic: tensor operations and devices
Combine tensor operations and devices in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Intermediate5 minComing Soon13 PyTorch: Configure dataset and dataloader for a small case
Topic: dataset and dataloader
Set up dataset and dataloader in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Intermediate6 minComing Soon14 PyTorch: Validate inputs around module and parameter registration
Topic: module and parameter registration
Add a check around module and parameter registration in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Intermediate7 minComing Soon15 PyTorch: Use forward pass and loss in a short workflow
Topic: forward pass and loss
Add forward pass and loss in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Intermediate8 minComing Soon16 PyTorch: Refactor autograd and gradients into a reusable unit
Topic: autograd and gradients
Extract autograd and gradients in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Intermediate9 minComing Soon17 PyTorch: Exercise optimizer step loop with sample data
Topic: optimizer step loop
Run sample data through optimizer step loop in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Intermediate10 minComing Soon18 PyTorch: Observe evaluation mode and inference outputs
Topic: evaluation mode and inference
Capture the result of evaluation mode and inference in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Intermediate3 minComing Soon19 PyTorch: Recover from a checkpoint save and restore failure
Topic: checkpoint save and restore
Handle one expected failure from checkpoint save and restore in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Intermediate5 minComing Soon20 PyTorch: Document reproducible training checks behavior
Topic: reproducible training checks
Write a short usage note for reproducible training checks in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Intermediate6 minComing Soon
Advanced
Lessons 21–30
21 PyTorch: Test a boundary around tensor shapes and dtypes
Topic: tensor shapes and dtypes
Add an edge-case test for tensor shapes and dtypes in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Advanced4 minComing Soon22 PyTorch: Harden tensor operations and devices
Topic: tensor operations and devices
Add the relevant access, safety, or validation control to tensor operations and devices in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Advanced5 minComing Soon23 PyTorch: Measure dataset and dataloader
Topic: dataset and dataloader
Record a small baseline for dataset and dataloader in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Advanced6 minComing Soon24 PyTorch: Reduce cost in module and parameter registration
Topic: module and parameter registration
Change one measurable cost in module and parameter registration in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Advanced7 minComing Soon25 PyTorch: Diagnose a forward pass and loss failure
Topic: forward pass and loss
Use an observable signal to explain a failure in forward pass and loss in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Advanced8 minComing Soon26 PyTorch: Make autograd and gradients repeatable
Topic: autograd and gradients
Show that rerunning autograd and gradients in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Advanced9 minComing Soon27 PyTorch: Review trade-offs in optimizer step loop
Topic: optimizer step loop
Compare two documented approaches to optimizer step loop in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Advanced10 minComing Soon28 PyTorch: Improve evaluation mode and inference maintainability
Topic: evaluation mode and inference
Refine evaluation mode and inference in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Advanced3 minComing Soon29 PyTorch: Verify checkpoint save and restore end to end
Topic: checkpoint save and restore
Check that a complete small flow using checkpoint save and restore in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Advanced5 minComing Soon30 PyTorch: Audit reproducible training checks against its source docs
Topic: reproducible training checks
Check the implementation of reproducible training checks in the context of PyTorch tensors, autograd, and training loops; save a short example or check that demonstrates the result.
Advanced6 minComing Soon
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