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PyTorch Beginner
Meet PyTorch the friendly way: tensors and shapes, autograd, modules, losses and optimizers, the training loop and data handling — finishing with a trained classifier and a capstone program. Assumes basic Python; no ML background needed.
Start learning ❯❯79 lessons · 19 topics · 0/79 completed
Course contents
Work top to bottom — the sidebar is the course. Topics later in the list assume the ones before them.
PyTorch Intro
Beginner · ⏱ 5 min · standalone lesson
PyTorch Installation
Beginner · ⏱ 6 min · standalone lesson
Creating Tensors
- PyTorch Creating Tensors
- PyTorch Tensor Data Types
- PyTorch Tensor Shapes
- PyTorch Tensors from Data
- PyTorch Random Tensors
- PyTorch Zeros and Ones
- PyTorch Tensor Ranges
Working with Shapes
- PyTorch Indexing Tensors
- PyTorch Slicing Tensors
- PyTorch Reshaping Tensors
- PyTorch Squeeze and Unsqueeze
- PyTorch Transpose and Permute
- PyTorch Flattening Tensors
Tensor Maths
- PyTorch Elementwise Operations
- PyTorch Tensor Comparisons
- PyTorch Tensor Reductions
- PyTorch Broadcasting
- PyTorch Matrix Multiplication
- PyTorch In-Place Operations
PyTorch Reading Tensor Errors
Beginner · ⏱ 6 min · standalone lesson
Selecting and Combining
Start: PyTorch Boolean Masking ❯Moving and Saving Tensors
- PyTorch Tensor Devices
- PyTorch Saving Tensors
- PyTorch Tensors to Python Values
- PyTorch Copying Tensors
Autograd
- PyTorch What Is Autograd
- PyTorch requires_grad
- PyTorch Computing Gradients
- PyTorch Gradients of Vectors
- PyTorch Stopping Gradients
- PyTorch Gradients Accumulate
PyTorch A Model by Hand
Beginner · ⏱ 10 min · standalone lesson
Neural Network Modules
- PyTorch What Is a Module
- PyTorch Linear Layers
- PyTorch Layer Shapes
- PyTorch Activation Functions
- PyTorch Sequential Models
- PyTorch Model Parameters
- PyTorch Your First Network
Loss Functions
- PyTorch What Is a Loss
- PyTorch Mean Squared Error
- PyTorch Other Regression Losses
- PyTorch Cross-Entropy Loss
- PyTorch Softmax and Probabilities
Optimizers
- PyTorch What Is an Optimizer
- PyTorch The SGD Optimizer
- PyTorch The Adam Optimizer
- PyTorch The Learning Rate
The Training Loop
- PyTorch Anatomy of a Training Loop
- PyTorch Training a Regressor
- PyTorch Train and Eval Modes
- PyTorch Measuring Accuracy
- PyTorch Saving and Loading Models
Preparing Features
- PyTorch Type Casting Tensors
- PyTorch Labels and Targets
- PyTorch One-Hot Encoding
- PyTorch Scaling Features
Data for Training
- PyTorch What Is a Dataset
- PyTorch Mini-Batches by Hand
- PyTorch The Dataset Class
- PyTorch TensorDataset
- PyTorch The DataLoader
- PyTorch Shuffling and Batches
- PyTorch Splitting Data
Watching Training
- PyTorch Epochs and Steps
- PyTorch The Validation Loop
- PyTorch Printing a Loss Curve
- PyTorch Overfitting, a First Look
A First Classifier
- PyTorch Making Blob Data
- PyTorch A Binary Classifier
- PyTorch Training the Classifier
- PyTorch Reading the Results
- PyTorch What the Model Learned
PyTorch Beginner Capstone
Beginner · ⏱ 12 min · standalone lesson