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 ...

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 ...

Stochastic gradient descent and Adam are optimization algorithms that update model parameters from estimated gradients, but they use different rules for momentum and per-parameter step sizes. This ...

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 ...