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Module 2 – torch.nn: Building Neural Networks

"From mere tensors we forge SENTIENT NETWORKS! Witness the birth of computational consciousness!"

— Prof. Torchenstein

Welcome back, my diligent apprentices! Having mastered the fundamental art of tensor manipulation, you are now ready for the next phase of your transformation: breathing life into neural architectures! In this module, we shall wield torch.nn as both scalpel and forge, assembling layers and models worthy of legend! ⚡️🧪

PyTorch Neural Networks

What Awaits in This Module

In Module 2, we transition from raw tensor operations to the high-level building blocks that make PyTorch a joy to work with:

  • Construct modular architectures using nn.Module as your blueprint
  • Layer upon layer – from humble linear transformations to exotic normalization techniques
  • Activate with purpose – ReLU, GELU, SiLU, and their mathematical rationale
  • Encode position and meaning with embeddings and positional encodings
  • Normalize like a pro – BatchNorm, LayerNorm, RMSNorm, and when to use each
  • Master the training cycle – understanding loss functions, training vs. eval modes, and preparing data

Rebel Mission Checklist 📝

The nn.Module Blueprint

  1. Building Brains with nn.Module - Craft custom neural matter by overriding __init__ and forward
  2. Franken-Stacking Layers - Bolt modules together with Sequential, ModuleList, and ModuleDict
  3. Preserving Your Monster's Memories - Save and resurrect model weights with state_dict necromancy

Linear Layers and Activations

  1. Linear Layers: The Vector Guillotine - Slice through dimensions, turning inputs into finely-chopped activations
  2. Activation Elixirs - Re-animate neurons with ReLU, GELU, SiLU, and other zesty potions
  3. Dropout: Neural Regularization - Make neurons forget just enough to generalize

Embeddings and Positional Encoding

  1. Embedding Layers: Secret Identity Chips - Embed discrete meanings within high-dimensional space
  2. Positional Encoding: Injecting Order - Imbue sequences with a sense of place so attention never loses its bearings

Normalization Techniques

  1. Normalization: Calming the Beast - Tame activations with BatchNorm and LayerNorm before they explode
  2. RMSNorm & Other Exotic Tonics - Sample contemporary concoctions for stable training
  3. Train vs. Eval: Split Personalities - Toggle modes and avoid awkward identity crises

Loss Functions and Training

  1. Loss Potions: Guiding Pain into Progress - Channel model errors into gradients that sharpen intelligence
  2. Preparing Sacrificial Inputs & Targets - Align logits and labels for maximum learning agony
  3. Reduction Rituals & Ignore Indices - Decipher reduction modes and skip unworthy samples

With these tools in hand, you will no longer be a mere tensor wrangler—you will be an architect of intelligence! The path ahead is electric with possibility. Steel your nerves, charge your GPUs, and prepare for computational glory!

Begin Your Transformation with nn.Module!