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.
0.5.1
VAEs
Implement autoencoders and VAEs, and explore their latent spaces.
0.5.2
GANs
Implement and train a DCGAN to generate celebrity faces.