A deep learning theory for neural networks grounded in physics

Understand

In the last decade, deep learning has become a major component of artificial intelligence.

• The workhorse of deep learning is the optimization of loss functions by stochastic gradient descent (SGD).
• Traditionally in deep learning, neural networks are differentiable mathematical functions, and the loss gradients required for SGD are computed with the backpropagation algorithm.
• However, the computer architectures on which these neural networks are implemented and trained suffer from speed and energy inefficiency issues, due to the separation of memory and processing in these architectures.