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 blog.demofox.org
Derivatives, Gradients, Jacobians and Hessians - Oh My! « The blog at the bottom of the sea
| | www.hhyu.org
Gradient descent on a non-Euclidean surface
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| | Science, programming, books, and other interesting stuff
| | jingnanshi.com
Automatic Differentiation: Forward and Reverse
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| | Tutorial on automatic differentiation
| | bytepawn.com
Bytepawn - Marton Trencseni - PyTorch Basics: Solving the Ax=b matrix equation with gradient descent
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| | I will show how to solve the standard A x = b matrix equation with PyTorch. This is a good toy problem to show some guts of the framework without involving neural networks.
| | blog.fastforwardlabs.com
"Hello world" in Keras (or, Scikit-learn versus Keras)
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| This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra.