Course · Advanced
Deep Learning Applications in PyTorch
Once you understand the fundamentals of deep learning, the next challenge is knowing how those concepts apply across different problem domains. PyTorch is widely used for tasks like modeling sequences, analyzing text, and working with images—but each area comes with its own patterns, architectures, and considerations. This course provides a practical overview of how deep learning techniques are applied in PyTorch across common domains, helping you recognize when to use different model types and how real-world deep learning problems are structured.
- Advanced friendly
- 8 hrs
- 4 lessons
- Premium
Course overview
Explore how PyTorch is used across major deep learning application areas including sequence models, natural language processing, and computer vision.
What's inside
4 lessons
- 01 120 minSequence Models in PyTorch
This hands-on guide walks through building sequence models in PyTorch to predict cinema ticket sales and explains why order matters in data.
- 02 120 minNatural Language Processing (NLP) with PyTorch
Learn how to build a real-world natural language processing (NLP) pipeline in PyTorch to classify tweets as disaster-related or not.
- 03 120 minComputer Vision in PyTorch (Part 1): Building Your First CNN for Pneumonia Detection
This beginner-friendly PyTorch tutorial covers CNN components, model architecture, and shape debugging with real-world medical data.
- 04 120 minComputer Vision in PyTorch (Part 2): Preparing Data, Training, and Evaluating Your CNN for Pneumonia Detection
Continue your computer vision project in PyTorch by preparing X-ray data, training your CNN, and evaluating model performance.
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