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Deep Learning
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?
Assembly: A Language Model You Can Interrogate
PACS — Building an Optimizer From Gradient Statistics
HRM — Hierarchical Reasoning With Fast and Slow Recurrent State
SICQL — Building a Model From Q, V and Policy Networks
Writing Neural Networks with PyTorch