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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
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© 2026 Ernan Hughes
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