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jingnanshi.com | ||
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comsci.blog
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| | | | | In this tutorial, we will learn two different methods to implement neural networks from scratch using Python: Extremely simple method: Finite difference Still a very simple method: Backpropagation | |
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matbesancon.xyz
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| | | | | What can automated gradient computations bring to mathematical optimizers, what does it take to compute? | |
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iclr-blogposts.github.io
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| | | | | The product between the Hessian of a function and a vector, the Hessian-vector product (HVP), is a fundamental quantity to study the variation of a function. It is ubiquitous in traditional optimization and machine learning. However, the computation of HVPs is often considered prohibitive in the context of deep learning, driving practitioners to use proxy quantities to evaluate the loss geometry. Standard automatic differentiation theory predicts that the computational complexity of an HVP is of the same order of magnitude as the complexity of computing a gradient. The goal of this blog post is to provide a practical counterpart to this theoretical result, showing that modern automatic differentiation frameworks, JAX and PyTorch, allow for efficient computation of these HVPs in standard deep learning cost functions. | |
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dennybritz.com
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| | | This the thirdpart of the Recurrent Neural Network Tutorial. | ||
