Course overview
Deep learning is applied differently depending on the type of problem you’re solving. In this course, you’ll explore how PyTorch is used across key application areas including sequence models, natural language processing, and computer vision. Rather than focusing on deep theory or production optimization, this course emphasizes understanding model structures, data representations, and common patterns so you can recognize how deep learning solutions are built in practice.
Key skills
- Explaining how PyTorch is used in major deep learning application areas
- Recognizing when to apply sequence models, NLP models, or computer vision models
- Understanding how input data shapes model design across domains
- Interpreting PyTorch model structures used in real-world deep learning projects
- Building intuition for selecting appropriate deep learning approaches for new problems
Course outline
Deep Learning Applications in PyTorch [4 lessons]
Sequence Models in PyTorch 2h
Lesson Objectives- Understand why sequential relationships matter in time-dependent data
- Implement RNN, LSTM, and GRU architectures using PyTorch
- Prepare time series data with proper windowing and scaling
- Prevent data leakage when splitting and normalizing sequences
- Apply optimization techniques like dropout and early stopping
Natural Language Processing (NLP) with PyTorch 2h
Lesson Objectives- Understand tokenization and convert text into numerical representations
- Load and fine-tune pretrained transformer models for classification
- Implement attention masks to handle variable-length text sequences
- Build complete PyTorch NLP pipelines with DataLoaders
- Evaluate model performance using F1 score and confusion matrices
Computer Vision in PyTorch (Part 1): Building Your First CNN for Pneumonia Detection 2h
Lesson Objectives- Explain how CNNs automatically extract important image features
- Understand core CNN components: convolutional and pooling layers
- Recognize object-oriented programming benefits in deep learning practice
- Define and build custom CNN architectures in PyTorch
- Debug tensor shape mismatches in neural network architectures
Computer Vision in PyTorch (Part 2): Preparing Data, Training, and Evaluating Your CNN for Pneumonia Detection 2h
Lesson Objectives- Download and verify chest X-ray dataset structure correctly
- Create validation sets using stratified splitting for imbalanced data
- Implement custom PyTorch Dataset classes with transformation pipelines
- Build complete training loops with proper device management
- Evaluate medical models using precision, recall, and confusion matrices
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