#009 PyTorch - How to apply Backpropagation With Vectors And Tensors

Highlights: In Machine Learning, a backpropagation algorithm is used to compute the loss for a particular model. The most common starting point is to use the techniques of single-variable calculus and understand how backpropagation works. However, the real challenge is when the inputs are not scalars but of matrices or tensors. In this post [1], we will learn how to deal with inputs like vectors, matrices, and tensors of higher ranks. We will understand how backpropagation with vectors and tensors is performed in computational graphs using single-variable as well as multi-variable derivatives.