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flexie committed Feb 22, 2024
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2 changes: 1 addition & 1 deletion .nojekyll
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11 changes: 3 additions & 8 deletions labs/01intro/underfitting_overfitting-preview.html
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4 changes: 2 additions & 2 deletions labs/01intro/underfitting_overfitting.out.ipynb
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"the validation set. The higher the value, the less likely the model\n",
"generalizes correctly from the training data since it is brittle."
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11 changes: 3 additions & 8 deletions labs/02Examples/ad/autograd_lin_reg-preview.html
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Expand Down Expand Up @@ -218,7 +218,7 @@ <h6><i class="bi bi-journal-code"></i> Linear Regression with <code>autograd</co
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4 changes: 2 additions & 2 deletions labs/02Examples/ad/autograd_lin_reg.out.ipynb
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"\n",
"In a later example, this will be done using `pytorch`."
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"Let us compare with `scikit_learn`"
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11 changes: 3 additions & 8 deletions labs/02Examples/ad/autograd_tut-preview.html
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Expand Down Expand Up @@ -218,7 +218,7 @@ <h6><i class="bi bi-journal-code"></i> <code>Autograd</code> tutorial</h6>
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16 changes: 8 additions & 8 deletions labs/02Examples/ad/autograd_tut.out.ipynb
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"\n",
"## Autograd Basic Usage"
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"> the perspective of computational complexity, the order makes all the\n",
"> difference."
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"The `@primitive` decorator wraps a function so that its gradient can be\n",
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"\\mathcal{E}(w,b) = \\frac{1}{N} \\sum_{i=1}^N \\mathcal{L}(y_i, t_i) = \\frac{1}{N} \\sum_{i=1}^N \\frac{1}{2} \\left(wx_i + b - t_i \\right)^2\n",
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"underlying regularity, but for which individual observations are\n",
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"\\mathbf{\\phi}(x) = \\begin{pmatrix}\\phi_1(x) \\\\ \\phi_2(x) \\\\ \\phi_3(x) \\\\ \\phi_4(x) \\end{pmatrix} = \\begin{pmatrix}1\\\\x\\\\x^2\\\\x^3\\end{pmatrix}\n",
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"automatically computes the backward pass. It essentially executes the\n",
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"\n",
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11 changes: 3 additions & 8 deletions labs/02Examples/ad/diff_prog-preview.html
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Expand Down Expand Up @@ -218,7 +218,7 @@ <h6><i class="bi bi-journal-code"></i> Intro Differentiable Programming</h6>
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8 changes: 4 additions & 4 deletions labs/02Examples/ad/diff_prog.out.ipynb
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"\n",
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"to complex and redundant expressions. In addition, balckbox routines\n",
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"Note that the derivative of $$ \\sigma(x) = \\frac{1}{1+e^{-x}}$$ can be\n",
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"\n",
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11 changes: 3 additions & 8 deletions labs/02Examples/pytorch/Torch_test_GPU_CPU-preview.html
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Expand Down Expand Up @@ -218,7 +218,7 @@ <h6><i class="bi bi-journal-code"></i> GPUs</h6>
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Expand Down Expand Up @@ -533,12 +533,7 @@ <h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
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8 changes: 4 additions & 4 deletions labs/02Examples/pytorch/Torch_test_GPU_CPU.out.ipynb
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"Here is a slightly more complex examples, with a matrix-vector tensor\n",
"multiplication."
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"Now, we drastically increase the problem size, using the tensor\n",
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"This is well known with GPUs: they are only faster if you put a large\n",
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11 changes: 3 additions & 8 deletions labs/02Examples/pytorch/pytorch_101-preview.html
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