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Master PyTorch with Adaptive AI.

Master PyTorch across 18 topics - Foundations, Intermediate, and Advanced - guided by Leyaa's adaptive intelligence.

3Parts
18Topics
137Patterns
35hEst. duration
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PyTorch
PyTorch Course
3 - part learning journey
Beginner
Foundations
6 topics · 50 patterns
Beginner
Intermediate
Intermediate
6 topics · 45 patterns
Intermediate
Advanced
Advanced
6 topics · 42 patterns
Advanced
Begin Part 1: Foundations
✦ Course Structure

Your PyTorch Journey in 3 Parts

Each part builds on the last - a deliberate progression from beginner to advanced.

Beginner
Beginner
Part 1: Foundations
Covers PyTorch Basics and Setup, Tensors, Autograd and Differentiation and 3 more topics.
6Topics
50Patterns
View topics
Intermediate
Intermediate
Part 2: Intermediate
Covers Convolutional Neural Networks, Recurrent Neural Networks, Transfer Learning and 3 more topics.
6Topics
45Patterns
View topics
Advanced
Advanced
Part 3: Advanced
Covers Custom Datasets and Transforms, Attention and Transformers, Generative Models and 3 more topics.
6Topics
42Patterns
View topics
Curriculum

Everything You'll Learn

Explore every topic and pattern in the course.

PyTorch

3 parts · 18 topics · 137 patterns
Beginner
Part 1: Foundations6 topics · 50 patterns
Beginner
01
PyTorch Basics and Setup
7 patterns
Why PyTorch is preferred for research and production
PyTorch vs TensorFlow comparison
Dynamic computation graph advantage
Installation and GPU setup
CUDA availability check
PyTorch ecosystem overview
First PyTorch computation
02
Tensors
10 patterns
Why tensors are PyTorch's core data structure
Tensor creation (torch.tensor, zeros, ones, rand)
Tensor shapes and dimensions
Tensor data types
Indexing and slicing
Reshaping (view, reshape, squeeze, unsqueeze)
Tensor operations (add, mul, matmul)
Broadcasting
NumPy bridge (from_numpy, numpy)
GPU tensors (to, cuda)
03
Autograd and Differentiation
8 patterns
Why automatic differentiation enables training
requires_grad flag
Forward pass computation
Backward pass (loss.backward)
Gradient access (.grad)
Gradient accumulation and zeroing
Detaching from computation graph
no_grad context manager
04
Building Neural Networks (nn.Module)
8 patterns
Why nn.Module organizes model code
Defining a model class
__init__ for layers
forward method
Linear (fully connected) layers
Activation functions (ReLU, Sigmoid, Softmax)
Sequential model shortcut
Model parameters inspection
05
Training Loop
9 patterns
Why the training loop is explicit in PyTorch
Loss functions (MSELoss, CrossEntropyLoss)
Optimizers (SGD, Adam)
Training loop structure
Forward pass, loss, backward, step
Zeroing gradients
Validation loop
Training and validation loss tracking
Epoch-based training
06
Data Loading
8 patterns
Why DataLoader handles batching and shuffling
Dataset class (custom datasets)
DataLoader basics
Batch size and shuffling
Built-in datasets (torchvision.datasets)
Data transforms
Train/val/test split
Num workers for parallel loading
Intermediate
Part 2: Intermediate6 topics · 45 patterns
Intermediate
01
Convolutional Neural Networks
8 patterns
Why CNNs detect spatial patterns
nn.Conv2d layers
Kernel size, stride, padding
nn.MaxPool2d and nn.AvgPool2d
CNN architecture for image classification
Flatten layer
Feature map visualization
Data augmentation with transforms
02
Recurrent Neural Networks
8 patterns
Why RNNs handle sequences
nn.RNN layer
nn.LSTM layer
nn.GRU layer
Hidden state management
Bidirectional RNNs
Sequence classification
Text preprocessing for RNNs
03
Transfer Learning
7 patterns
Why pre-trained models accelerate development
torchvision pre-trained models
Feature extraction strategy
Fine-tuning strategy
Freezing layers
Replacing classifier head
Learning rate differential
04
Regularization
7 patterns
Why regularization controls overfitting
Dropout (nn.Dropout)
Batch normalization (nn.BatchNorm)
Weight decay (L2 regularization)
Data augmentation
Early stopping implementation
Label smoothing
05
Learning Rate and Optimization
7 patterns
Why learning rate strategy affects convergence
Learning rate schedulers
StepLR and MultiStepLR
ReduceLROnPlateau
CosineAnnealingLR
Warmup strategies
Gradient clipping
06
Model Saving and Loading
8 patterns
Why checkpointing preserves progress
Saving model state_dict
Loading model state_dict
Saving entire model
Checkpoint with optimizer state
Best model saving pattern
ONNX export
TorchScript export
Advanced
Part 3: Advanced6 topics · 42 patterns
Advanced
01
Custom Datasets and Transforms
7 patterns
Why custom data pipelines handle real data
Custom Dataset class
__getitem__ and __len__
Image dataset from folders
Custom transforms
Compose transforms
Albumentations integration
02
Attention and Transformers
8 patterns
Why attention revolutionized deep learning
Self-attention mechanism
Multi-head attention
Positional encoding
Transformer encoder
Transformer decoder
Hugging Face integration basics
BERT for text classification
03
Generative Models
6 patterns
Why generative models create data
Autoencoder architecture
Variational Autoencoder
GAN training loop
Generator and discriminator
Image generation basics
04
Object Detection
7 patterns
Why detection localizes objects
torchvision detection models
Faster R-CNN usage
YOLO concept
Bounding box handling
Non-maximum suppression
Custom detection dataset
05
Distributed Training
6 patterns
Why distributed training handles large models
DataParallel basics
DistributedDataParallel
Multi-GPU training
Mixed precision training (AMP)
Gradient accumulation
06
Deployment
8 patterns
Why deployment serves predictions
TorchServe setup
Model packaging (.mar files)
REST API inference
TorchScript for production
ONNX Runtime inference
Mobile deployment (PyTorch Mobile)
Model optimization (quantization, pruning)
Why Leyaa

Not just a course. A learning intelligence.

Leyaa doesn't just deliver content - it understands where you are, what you need, and how to get you there fastest.

Adaptive Diagnostics
Adaptive Diagnostics
Leyaa identifies which of the 137 patterns you've mastered and which need work - before you waste time on what you already know.
Pattern-Level Precision
Pattern-Level Precision
Every pattern gets a verdict - Learn, Practice, or Skip - so your study time goes exactly where it counts.
Intelligent Pacing
Intelligent Pacing
Leyaa reads your velocity across the whole course and adapts your focus list dynamically, keeping you in the optimal learning zone.
Before & After Visibility
Before & After Visibility
Every assessment creates a snapshot. Retake anytime to see exactly how much you've grown - topic by topic, pattern by pattern.
Learner Autonomy
Learner Autonomy
Leyaa recommends, never restricts. Override any suggestion and explore at your own pace - the system tracks everything regardless.
Clear Learning Path
Clear Learning Path
Foundations → Intermediate → Advanced - a structured journey where each part sets you up perfectly for the next.
PyTorch

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18 Topics
137 Patterns
Personalized roadmap
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