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
Preference Rankers — Learning Which Answer Is Better
Cellular Automata From First Principles 51: Run Cellular Automata on the GPU
Cellular Automata From First Principles 48: What Did the Neural CA Actually Learn?
Cellular Automata From First Principles 47: Inspect Hidden-State Propagation
Cellular Automata From First Principles 43: Test Generalization Beyond Training
Cellular Automata From First Principles 46: Generalize to Harder and Larger Mazes
Cellular Automata From First Principles 42: Regenerate After Damage
Cellular Automata From First Principles 45: Learn to Solve Mazes
Cellular Automata From First Principles 41: Train for Persistence
Cellular Automata From First Principles 44: Neural Cellular Automata for Pathfinding
Cellular Automata From First Principles 40: Randomize the Update Schedule
Cellular Automata From First Principles 39: Grow a Target From One Seed
Cellular Automata From First Principles 38: Hidden Cell Channels and Local Memory
Cellular Automata From First Principles 37: Learn the Local Update Rule
Cellular Automata From First Principles 36: Make the Automaton Differentiable
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
MR.Q: Model-Based Representations for Model-Free Trading
Writing Neural Networks with PyTorch