When learning neural networks, terms like activation functions, loss functions, backpropagation, and gradient descent appear ...

Researchers have demonstrated a photonic chip that trains itself on the hardware using on-chip holography to compute physical gradients, achieving 0.26 percent error and dramatic gains in model ...

Source: Yoshitaka Tomiyama Published: 2020-03-04 / 44 minutes / 24,000 views Scope: The second in a series of explanations ...

Find out why backpropagation and gradient descent are key to prediction in machine learning, then get started with training a simple neural network using gradient descent and Java code. Most ...

Serving as a pivotal parameter during deep learning training processes, learning rate plays a crucial role regarding training efficiency. Traditional optimizers confront challenges such as sluggish ...

Quantization is an effective approach to accelerate deep neural networks by restricting their weights and activation functions to low precisions. However, the training objective (loss function) ...

The most widely used technique for finding the largest or smallest values of a math function turns out to be a fundamentally difficult computational problem. Many aspects of modern applied research ...

Your institution does not have access to this book on JSTOR. Try searching on JSTOR for other items related to this book. Gradient-Based Methods for Deterministic Continuous Optimization Chapter One ...

Gradient descent is an optimization algorithm that refines a machine learning model's parameters to create a more accurate model. The goal is to reduce a model's ...