PyTorch Essential Training: Deep Learning

Explore the basics of deep learning using PyTorch and test your knowledge with hands-on challenges.

Introduction

• Deep learning with PyTorch
• What you should know
• Tour of CoderPad

1. PyTorch Overview and Introduction to Google Colaboratory

• Introduction to deep learning
• Why should you use PyTorch
• Google Colaboratory basics

2. Tensors

• Introduction to tensors
• Creating a tensor CPU example
• Creating a tensor GPU example
• Moving tensors between CPUs and GPUs

3. Creating Tensors

• Different ways to create tensors
• Tensor attributes
• Tensor data types
• Creating tensors from random samples
• Creating tensors like other tensors
• Solution: Create tensors

4. Manipulate Tensors

• Tensor operations
• Mathematical functions
• Linear algebra operations
• Automatic differentiation (Autograd)
• Solution: Split tensors to form new tensors

5. Developing a Deep Learning Model

• Introduction to the DL training process
• Data preparation
• Data loading
• Data transforms
• Data batching
• Model development and training
• Validation and testing

Conclusion

• Next steps