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vxy10.github.io | ||
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robotchinwag.com
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| | | | | Deriving the gradients for the backward pass for matrix multiplication using tensor calculus | |
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glowingpython.blogspot.com
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| | | | | Using regularization has many benefits, the most common are reduction of overfitting and solving multicollinearity issues. All of this is co... | |
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dennybritz.com
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| | | | | All the code is also available as an Jupyter notebook on Github. | |
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theorydish.blog
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| | | The chain rule is a fundamental result in calculus. Roughly speaking, it states that if a variable $latex c$ is a differentiable function of intermediate variables $latex b_1,\ldots,b_n$, and each intermediate variable $latex b_i$ is itself a differentiable function of $latex a$, then we can compute the derivative $latex \frac{{\mathrm d} c}{{\mathrm d} a}$ as... | ||
