The calculus and linear algebra you need for deep learning
The calculus behind gradient descent
How derivatives compose through layers
Partial derivatives, gradients, and the Jacobian — calculus for vectors
Vectors, matrices, and the language of data
The data structures of deep learning
How matrices transform space — eigenvectors, eigenvalues, and decomposition