Chapter 0: Fundamentals
Build your foundation in deep learning, from prerequisites through CNNs, optimization, backpropagation, and generative models.
Sections
0.0
Prerequisites
Essential PyTorch basics, einops/einsum libraries, and tensor manipulation fundamentals.
0.1
Ray Tracing
Learn batched operations and linear algebra by rendering 3D meshes with raytracing.
0.2
CNNs & ResNets
Build neural networks from scratch, from MNIST classifiers to ResNets for CIFAR-10.
0.3
Optimization
Implement SGD, RMSprop & Adam optimizers, and use Weights & Biases for experiment tracking.
0.4
Backpropagation
Build your own autograd system from scratch and train MLPs with custom backpropagation.
0.5
VAEs & GANs
Implement GANs and VAEs, foundational architectures for generative image models.