Module 10: Deep Learning Frameworks
Master TensorFlow/Keras and PyTorch for building deep learning models.
What You'll Learn
- TensorFlow/Keras Basics
- PyTorch Basics
- Building and Training Neural Networks
- Model Saving and Loading
- Transfer Learning Basics
Deep learning curriculum map
| Topic | Where to study |
|---|---|
| PyTorch tensors, autograd, training pipeline | Frameworks guide |
| NumPy/theory first | Neural networks basics |
| MNIST, churn, house-price style ANN projects | Intermediate projects |
| CNN and RNN in PyTorch (vision + sequences) | CNN & RNN map |
Topics Covered
1. TensorFlow/Keras
- Keras API: High-level, user-friendly
- Sequential Model: Simple linear stack
- Functional API: Complex architectures
- Model Compilation: Loss, optimizer, metrics
- Training: fit() method
- Callbacks: Early stopping, model checkpointing
2. PyTorch
- Tensors: PyTorch's array type
- Autograd: Automatic differentiation
- nn.Module: Building blocks
- Training Loop: Manual control
- DataLoader: Efficient data loading
- GPU Support: CUDA operations
3. Model Building
- Layer Types: Dense, Conv2D, LSTM, etc.
- Regularization: Dropout, Batch Normalization
- Architecture Design: Choosing layers and sizes
4. Training Best Practices
- Data Preprocessing: Normalization, augmentation
- Validation: Monitor validation loss
- Early Stopping: Prevent overfitting
- Learning Rate Scheduling: Adaptive learning rates
- Model Checkpointing: Save best models
5. Model Deployment
- Saving Models: Save architecture and weights
- Loading Models: Restore for inference
- Model Export: TensorFlow Lite, ONNX
Learning Objectives
By the end of this module, you should be able to:
- Build neural networks with Keras
- Build neural networks with PyTorch
- Train models effectively
- Save and load models
- Apply transfer learning
Projects
- Keras vs PyTorch: Build same model in both frameworks
- Image Classification: CIFAR-10 with Keras
- Transfer Learning: Use pre-trained models
- Model Deployment: Save and serve models
Key Concepts
- Keras: Easier to learn, great for beginners
- PyTorch: More flexible, research-friendly
- Both are powerful: Choose based on preference/project
- Transfer Learning: Use pre-trained models (saves time!)
- GPU Acceleration: Essential for large models
Documentation & Learning Resources
TensorFlow/Keras:
- TensorFlow Official Documentation
- TensorFlow Tutorials
- Keras Documentation
- Keras Guide
- TensorFlow Guide
PyTorch:
Free Courses:
- Fast.ai Practical Deep Learning: Completely free, top-down approach
- Deep Learning Specialization (Coursera): Free audit available
- TensorFlow Developer Certificate Course: Free audit available
- PyTorch for Deep Learning (YouTube): Free course
Video Tutorials:
Practice:
Additional Resources:
- Advanced Topics →: Custom layers, advanced training, optimization, deployment
- Project Tutorial →: Step-by-step comparison of Keras and PyTorch
- Quick Reference →: Quick lookup guide for both frameworks
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