Backpropagation is an algorithm that efficiently calculates the gradient of the loss with respect to each and every parameter in a computation graph.

We will describe how automatic differentiation can be implemented on computational graphs, allowing backpropa-gation to be derived “for free” once a network has been expressed as a …

Backpropagation is the key algorithm that makes training deep models computationally tractable. For modern neural networks, it can make training with gradient descent as much as ten million …

Backpropagation 1. Identify intermediate functions (forward prop) 2. Compute local gradients 3. Combine with upstream error signal to get full gradient

A dynamic programming algorithm on computation graphs that allows the gradient of an output to be computed with respect to every node in the graph 13

•Backpropagation •Easy to understand and implement •Bad for memory use and schedule optimization •Automatic differentiation •Generate gradient computation to entire computation …

Backpropagation (\backprop" for short) is. way of computing the partial derivatives of a loss function with respect to the parameters of a network; we use these derivatives in gradient …

In this chapter, we will introduce the fundamental concepts that underpin all of deep learning - computational graphs and backpropagation. To showcase these ideas we will create and train …

Overview: Backpropagation • Computation graphs • Using the chain rule • General backprop algorithm • Toy examples of backward pass • Matrix-vector calculations: ReLU, linear layer

In this section, we take a deep dive into the details of backward propagation (more commonly called backpropagation). To convey some insight for both the techniques and their …