Training Neural Networks foundational
How forward passes, losses, backpropagation, optimization, and regularization work together so the architecture
How forward passes, losses, backpropagation, optimization, and regularization work together so the architecture
Register to Read
Sign up for a free account to access all 112 primer topics.
Create Free AccountAlready have an account? Sign in
References
Adam: A Method for Stochastic Optimization
Diederik P. Kingma, Jimmy Ba
(2017)
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot, Yoshua Bengio
(2010)
— Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS)
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal, Zoubin Ghahramani
(2016)
— PMLR