- 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
- PyTorch Zero to Hero 10: Build a Small GPT-Style Language Model From Scratch
- PyTorch Performance Debugging: CUDA OOM, Slow Training, GPU Utilization and torch.compile
- PyTorch Model Not Learning? A Systematic Debugging Guide
- PyTorch Attention Shapes: Q, K, V, Multi-Head Attention Masks and Transformer Dimension Errors
- PyTorch CNN Shape Errors: Conv2d Output Sizes, Channels, Flatten Bugs and How to Debug Them
- PyTorch DataLoader Performance: num_workers, pin_memory, Prefetching and Why Your GPU Is Waiting
- PyTorch nn.Module Explained: Missing Parameters, state_dict, Buffers and Registration Bugs
- Build a Neural Network From Scratch in PyTorch Without nn.Module
- PyTorch Autograd Debugging: requires_grad, detach, backward() and NaN Gradients
- PyTorch Tensor Shapes: Broadcasting, Reshape, View, Permute and the Errors That Waste Your Time
- PyTorch Zero to Hero 00: What Are We Actually Doing?
- Writing Neural Networks with PyTorch