# Tag Info

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Something that I personally use is Google Trends. This is a very useful tool for verifying the interest of a broad public on some subject. Results can even be refined to include region and/or time span. For instance, here you can see a comparison for the interest in Tensorflow, Keras and Pytorch over the past 12 months:

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XPU is a device abstraction for Intel heterogeneous computation architectures, which can be mapped to CPU, GPU, FPGA and other accelerators. The "X" from XPU is just like a variable, like in maths, so you can do X=C and you get CPU accceleration, or X=G and you get GPU acceleration... That's the intuition behind that abstract name. In order to ...

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Yes, it is a bit misleading. What it really means is input channels, so it would be: nn.Conv2d: Applies a 2D convolution over an input signal composed of several input channels. So, why don't just use channels instead of input planes? Well, initially the major deep learning applications were used for computer vision or image processing approaches. In CV or ...

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It’s a tradeoff allowing you to fit a larger model into a fixed RAM budget (ie the size of your GPU). Whether this is a good tradeoff is model- and data-specific, but anecdotally I’ve had good luck with it and usually use half precision to good effect (NLP, mostly).

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The fact is you can always express an affine transformation as a linear transformation (more convenient because it is just a matrix/dot product). For instance, given an input $\textbf{x}=[x_1, ..., x_n]$, some weights $\textbf{a} = [a_1, a_2, ..., a_n]$ and a bias $b \in \mathbb{R}$, you can express the affine operation $y = \textbf{a}\cdot \textbf{x} + b$ ...

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In the automatic differentiation procedure after backward pass the gradient with respect to the scalar is added to the current gradient. Without calling zero_grad you will have the sum of all gradients, calcluated before, with the current gradient. Therefore, optimizer.step() will do not this: w = w - eta * grad L[i] # L[i] - loss function for the i-th ...

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In linear algebra, a linear transformation (aka linear map or linear transform) $f: \mathcal{V} \rightarrow \mathcal{W}$ is a function that satisfies the following two conditions $f(u + v)=f(u)+f(v)$ (additivity) $f(\alpha u) = \alpha f(u)$ (scalar multiplication), where $u$ and $v$ vectors (i.e. elements of a vector space, which can also be $\mathbb{R}$ [...

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You can see some labels at https://www.tensorflow.org/datasets/catalog/emnist. It goes like this: ‘0’-‘9’ are 0-9 ‘A’-‘Z’ are 10-35 ‘a’-‘z’ are 36-61

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