Deep Learning Essentials
At some point in time, I'm sure we have all been unable to understand an article on substack or a paper on arXiv because the theory or the terminology is confusing. Through this short article, I hope to demystify, to a certain degree, some simple theory and jargon behind deep learning. At the very least, if you encounter some new obscure theory in the future, you should be able to know where to look.
To begin your deep learning studying journey, start here: Introduction.
Contents
Introduction
Training
Tensors
Hardware
All Pages
April 7, 2024
Autoregressive Models
April 7, 2024
Backpropagation
April 7, 2024
Capacity
April 7, 2024
Contrastive Loss
April 7, 2024
Cross Entropy Loss
April 7, 2024
Gradient Descent and Stochastic Gradient Descent
April 7, 2024
Graphical Processing Units
April 7, 2024
Introduction
April 7, 2024
Loss
April 7, 2024
Mean Squared Error
April 7, 2024
Optimization
April 7, 2024
Training Protocols
April 7, 2024
Tensors
April 7, 2024
Training
April 7, 2024
The Vanishing Gradient Problem