Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

There are MANY different layers that can be used in PyTorch:
• Convolution Layers
• Pooling Layers
• Padding Layers
• Non-linear Activations
• Normalization Layers
• Recurrent Layers
• Transformer Layers
• Linear Layers
• Dropout Layers
• Sparse Layers (embeddings)
• Vision Layers
• DataParallel Layers (multi-GPU)
• Flatten Layer
So far, we have just used a Linear layer. In the chapters ahead, we’ll use many
others, like convolution, pooling, padding, flatten, dropout, and non-linear
activations.
Putting It All Together
We’ve covered a lot of ground so far, from coding a linear regression in Numpy
using gradient descent to transforming it into a PyTorch model, step-by-step.
It is time to put it all together and organize our code into three fundamental parts,
namely:
• data preparation (not data generation!)
• model configuration
• model training
Let’s tackle these three parts, in order.
Putting It All Together | 115