From 35bbac271b46da656ac102f20041dffcd59bb57e Mon Sep 17 00:00:00 2001 From: udlbook <110402648+udlbook@users.noreply.github.com> Date: Tue, 14 Nov 2023 09:09:49 +0000 Subject: [PATCH] Update index.html --- index.html | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/index.html b/index.html index d80b033..0bb84b5 100644 --- a/index.html +++ b/index.html @@ -343,29 +343,29 @@
  • Notebook 13.4 - Graph attention: ipynb/colab
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  • Notebook 15.1 - GAN toy example: ipynb/colab a
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  • Notebook 15.2 - Wasserstein distance: ipynb/colab a
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  • Notebook 16.1 - 1D normalizing flows: ipynb/colab a
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  • Notebook 16.2 - Autoregressive flows: ipynb/colab a
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  • Notebook 16.3 - Contraction mappings: ipynb/colab a
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  • Notebook 17.1 - Latent variable models: ipynb/colab a
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  • Notebook 17.2 - Reparameterization trick: ipynb/colab a
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  • Notebook 17.3 - Importance sampling: ipynb/colab a
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  • Notebook 18.1 - Diffusion encoder: ipynb/colab a/li> -
  • Notebook 18.2 - 1D diffusion model: ipynb/colab a
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  • Notebook 18.3 - Reparameterized model: ipynb/colab a
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  • Notebook 18.4 - Families of diffusion models: ipynb/colab a
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  • Notebook 19.1 - Markov decision processes: ipynb/colab a
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  • Notebook 19.2 - Dynamic programming: ipynb/colab a
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  • Notebook 19.3 - Monte-Carlo methods: ipynb/colab a
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  • Notebook 19.4 - Temporal difference methods: ipynb/colab a
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  • Notebook 19.5 - Control variates: ipynb/colab a
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  • Notebook 20.1 - Random data: ipynb/colab a/li> -
  • Notebook 20.2 - Full-batch gradient descent: ipynb/colab a/li> -
  • Notebook 20.3 - Lottery tickets: ipynb/colab a/li> -
  • Notebook 20.4 - Adversarial attacks: ipynb/colab a
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  • Notebook 21.1 - Bias mitigation: ipynb/colab a
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  • Notebook 21.2 - Explainability: ipynb/colab a
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  • Notebook 15.1 - GAN toy example: ipynb/colab a/li> +
  • Notebook 15.2 - Wasserstein distance: ipynb/colab
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  • Notebook 16.1 - 1D normalizing flows: ipynb/colab
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  • Notebook 16.2 - Autoregressive flows: ipynb/colab
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  • Notebook 16.3 - Contraction mappings: ipynb/colab
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  • Notebook 17.1 - Latent variable models: ipynb/colab
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  • Notebook 17.2 - Reparameterization trick: ipynb/colab
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  • Notebook 17.3 - Importance sampling: ipynb/colab
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  • Notebook 18.1 - Diffusion encoder: ipynb/colab /li> +
  • Notebook 18.2 - 1D diffusion model: ipynb/colab
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  • Notebook 18.3 - Reparameterized model: ipynb/colab
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  • Notebook 18.4 - Families of diffusion models: ipynb/colab
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  • Notebook 19.1 - Markov decision processes: ipynb/colab
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  • Notebook 19.2 - Dynamic programming: ipynb/colab
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  • Notebook 19.3 - Monte-Carlo methods: ipynb/colab
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  • Notebook 19.4 - Temporal difference methods: ipynb/colab
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  • Notebook 19.5 - Control variates: ipynb/colab
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  • Notebook 20.1 - Random data: ipynb/colab
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  • Notebook 20.2 - Full-batch gradient descent: ipynb/colab /li> +
  • Notebook 20.3 - Lottery tickets: ipynb/colab /li> +
  • Notebook 20.4 - Adversarial attacks: ipynb/colab a/li> +
  • Notebook 21.1 - Bias mitigation: ipynb/colab
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  • Notebook 21.2 - Explainability: ipynb/colab