PyTorch

What Are We Actually Doing?
The Tensor: What Is Actually Flowing Through the Loop?
Autograd: What Did PyTorch Record, and Where Does the Gradient Stop?
The Network: What Is It Without nn.Module?
nn.Module: What Does PyTorch Think Belongs to Your Model?
DataLoader: Where Is the Training Loop Actually Waiting?
Transforms: What Does the Model Actually See?
CNN Geometry: What Shape Reaches the Next Layer?
Feature Space: What Does a Linear Model Actually See?
Attention: Which Position Is Comparing With Which?
Training: Which Link in the Learning Chain Is Broken?
Performance: What Is the Machine Waiting For?
Compilation: Which Assumption Stopped Holding?
Regressions: Did the Model Change, or the Measurement?
Assembly: A Language Model You Can Interrogate
Appendix A: PyTorch Diagnostic Field Guide
From Vectors to Symbols — The Binding Problem Inside Neural Networks
Reasoning Is More Than Architecture — Where Extra Computation Lives
Preference Rankers — Learning Which Answer Is Better
Which Model Should You Use? MR.Q, EBT, SICQL, HRM, Tiny and PACS Compared
PACS — Building an Optimizer From Gradient Statistics
Inside Tiny — Residual Blocks, Attention and Sparse Autoencoders
Tiny — Recursive Reasoning With a Small Neural Network
HRM — Hierarchical Reasoning With Fast and Slow Recurrent State
SICQL — Building a Model From Q, V and Policy Networks
EBT — From One Score to Q, V, Policy and Advantage
MR.Q — Building a Neural Quality Model From Two Embeddings
The Model Inside the Model