repo name

Author: vpj

docs/capsule_networks/index.html

@@ -78,7 +78,7 @@ <h1>Capsule Networks</h1>
 <p>I used <a href="https://github.com/jindongwang/Pytorch-CapsuleNet">jindongwang/Pytorch-CapsuleNet</a> to clarify some
 confusions I had with the paper.</p>
 <p>Here&rsquo;s a notebook for training a Capsule Network on MNIST dataset.</p>
-<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/capsule_networks/mnist.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/capsule_networks/mnist.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
 <a href="https://app.labml.ai/run/e7c08e08586711ebb3e30242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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             <div class='code'>

docs/capsule_networks/readme.html

@@ -78,7 +78,7 @@ <h1><a href="https://nn.labml.ai/capsule_networks/index.html">Capsule Networks</
 <p>I used <a href="https://github.com/jindongwang/Pytorch-CapsuleNet">jindongwang/Pytorch-CapsuleNet</a> to clarify some
 confusions I had with the paper.</p>
 <p>Here&rsquo;s a notebook for training a Capsule Network on MNIST dataset.</p>
-<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/capsule_networks/mnist.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/capsule_networks/mnist.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
 <a href="https://app.labml.ai/run/e7c08e08586711ebb3e30242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
             </div>
             <div class='code'>

docs/cfr/index.html

@@ -78,7 +78,7 @@ <h1>Regret Minimization in Games with Incomplete Information (CFR)</h1>
 where we sample from the game tree and estimate the regrets.</p>
 <p>We tried to keep our Python implementation easy-to-understand like a tutorial.
 We run it on <a href="kuhn/index.html">a very simple imperfect information game called Kuhn poker</a>.</p>
-<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/cfr/kuhn/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/cfr/kuhn/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
 <p><a href="https://twitter.com/labmlai/status/1407186002255380484"><img alt="Twitter thread" src="https://img.shields.io/twitter/url?style=social&amp;url=https%3A%2F%2Ftwitter.com%2Flabmlai%2Fstatus%2F1407186002255380484" /></a>
 Twitter thread</p>
 <h2>Introduction</h2>

docs/cfr/kuhn/index.html

@@ -88,7 +88,7 @@ <h1><a href="../index.html">Counterfactual Regret Minimization (CFR)</a> on Kuhn
 </ul>
 <p>He we extend the <code>InfoSet</code> class and <code>History</code> class defined in <a href="../index.html"><code>__init__.py</code></a>
 with Kuhn Poker specifics.</p>
-<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/cfr/kuhn/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/cfr/kuhn/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
 <a href="https://app.labml.ai/run/7c35d3fad29711eba588acde48001122"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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docs/gan/cycle_gan/index.html

@@ -84,7 +84,7 @@ <h1>Cycle GAN</h1>
 The discriminators test whether the generated images look real.</p>
 <p>This file contains the model code as well as the training code.
 We also have a Google Colab notebook.</p>
-<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/gan/cycle_gan/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/gan/cycle_gan/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
 <a href="https://app.labml.ai/run/93b11a665d6811ebaac80242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
             </div>
             <div class='code'>

docs/gan/wasserstein/index.html

@@ -133,7 +133,7 @@ <h1>Wasserstein GAN (WGAN)</h1>
 while keeping $K$ bounded. <em>One way to keep $K$ bounded is to clip all weights in the neural
 network that defines $f$ clipped within a range.</em></p>
 <p>Here is the code to try this on a <a href="experiment.html">simple MNIST generation experiment</a>.</p>
-<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/gan/wasserstein/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/gan/wasserstein/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
             </div>
             <div class='code'>
                 <div class="highlight"><pre><span class="lineno">87</span><span></span><span class="kn">import</span> <span class="nn">torch.utils.data</span>

docs/hypernetworks/hyper_lstm.html

@@ -74,7 +74,7 @@ <h1>HyperNetworks - HyperLSTM</h1>
 by David Ha gives a good explanation of HyperNetworks.</p>
 <p>We have an experiment that trains a HyperLSTM to predict text on Shakespeare dataset.
 Here&rsquo;s the link to code: <a href="experiment.html"><code>experiment.py</code></a></p>
-<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/hypernetworks/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/hypernetworks/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
 <a href="https://app.labml.ai/run/9e7f39e047e811ebbaff2b26e3148b3d"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
 <p>HyperNetworks use a smaller network to generate weights of a larger network.
 There are two variants: static hyper-networks and dynamic hyper-networks.

docs/index.html

@@ -68,7 +68,7 @@
                 <h1><a href="index.html">labml.ai Annotated PyTorch Paper Implementations</a></h1>
 <p>This is a collection of simple PyTorch implementations of
 neural networks and related algorithms.
-<a href="https://github.com/lab-ml/nn">These implementations</a> are documented with explanations,
+<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations">These implementations</a> are documented with explanations,
 and the <a href="index.html">website</a>
 renders these as side-by-side formatted notes.
 We believe these would help you understand these algorithms better.</p>

docs/normalization/batch_channel_norm/index.html

@@ -77,7 +77,7 @@ <h1>Batch-Channel Normalization</h1>
 batch normalization.</p>
 <p>Here is <a href="../weight_standardization/experiment.html">the training code</a> for training
 a VGG network that uses weight standardization to classify CIFAR-10 data.</p>
-<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/normalization/weight_standardization/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/normalization/weight_standardization/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
 <a href="https://app.labml.ai/run/f4a783a2a7df11eb921d0242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a>
 <a href="https://wandb.ai/vpj/cifar10/runs/3flr4k8w"><img alt="WandB" src="https://img.shields.io/badge/wandb-run-yellow" /></a></p>
             </div>

docs/normalization/batch_norm/index.html

@@ -132,7 +132,7 @@ <h2>Inference</h2>
 mean and variance during the training phase and use that for inference.</p>
 <p>Here&rsquo;s <a href="mnist.html">the training code</a> and a notebook for training
 a CNN classifier that uses batch normalization for MNIST dataset.</p>
-<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/normalization/batch_norm/mnist.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
+<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/normalization/batch_norm/mnist.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
 <a href="https://app.labml.ai/run/011254fe647011ebbb8e0242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
             </div>
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