Learn PyTorch for Deep Learning
Zero to Mastery Course
Course Overview
Learn PyTorch foundations for machine learning and deep learning through hands-on, code-first approach.
Course Modules
00. PyTorch Fundamentals
- Tensor operations
- PyTorch workflow
- GPU acceleration
01. Neural Network Regression
- Linear regression
- Loss functions
- Optimization
02. Neural Network Classification
- Binary & multi-class classification
- Activation functions
- Evaluation metrics
03. Computer Vision
- CNNs for image classification
- Transfer learning basics
- Model evaluation
04. Custom Datasets
- Data loading
- Transforms
- DataLoaders
05. Going Modular
- Production code structure
- Reusable components
- Best practices
06. Transfer Learning
- Pre-trained models
- Fine-tuning
- Feature extraction
07. Experiment Tracking
- Logging experiments
- Comparing models
- TensorBoard integration
08. Paper Replicating
- Read research papers
- Implement architectures
- Reproduce results
09. Model Deployment
- Save/load models
- Serve predictions
- Deploy to production
Learning Approach
- Code Along: Write PyTorch code yourself
- Experiment: Try variations and modifications
- Visualize: Make concepts visual
- Ask Questions: Use community resources
- Do Exercises: Practice with problems
- Share Work: Build in public
Prerequisites
- 3-6 months Python
- Basic ML knowledge
- Jupyter/Colab experience
- Willingness to learn
Resources
- All materials free online
- Video course on ZTM Academy
- GitHub repository
- Active Discord community
Learn PyTorch through hands-on practice and real-world projects.