Tensor math is linear algebra on steroids. Here are some valuable resources to understand it better.

TODO: Add all of the tensor series.

Ouput embeddings of two samples will be i.I.d. under randompermutations. Introduces generalization to Tensors and creates NETSOR Computation Programs Introduces three general mapping types of function variables.

NETSOR programs are straight-line programs, where each variable follows one of three types, G, H, or A (such variables are called G-vars, H-vars, and A-vars), and after input variables, new variables can be introduced by one of the rules MatMul, LinComb, Nonlin to be discussed shortly. G and H are vector types and A is a matrix type; intuitively, G-vars should be thought of as vectors that are asymptotically Gaussian, H-vars are images of G-vars by coordinatewise nonlinearities, and A-vars are random matrices with iid Gaussian entries. Each type is annotated by dimensionality information:

If x is a (vector) variable of type G (or H) and has dimension n, we write x : G(n) (or x : H(n)).
If A is a (matrix) variable of type A and has size n1 × n2, we write A : A(n1, n2)
G is a subtype of H, so that x : G(n) implies x : H(n).

G is petty much a ‘pass through’ like an activation function.

This a Github implementation

Tensor Programs IVb: Adaptive Optimization in the ∞-Width Limit Demonstrates how to scale hyperparameters when changing widths of feature parameters generally

Micro update

"We show that optimal hyperparameters become stable across neural network sizes when we parametrize the model in maximal update parametrization (μP). This can be used to tune extremely large neural networks such as large pretrained transformers, as we have done in our work. More generally, μP reduces the fragility and uncertainty when transitioning from exploration to scaling up, which are not often talked about explicitly in the deep learning literature."

Tensor Maths