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<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/__init__.py">
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<h1><a href="index.html">labml.ai Annotated PyTorch Paper Implementations</a></h1>
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<p>This is a collection of simple PyTorch implementations of
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neural networks and related algorithms.
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<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations">These implementations</a> are documented with explanations,
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and the <a href="index.html">website</a>
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renders these as side-by-side formatted notes.
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We believe these would help you understand these algorithms better.</p>
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<p>We are actively maintaining this repo and adding new
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implementations.</p>
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<h2>Modules</h2>
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<h4>✨ <a href="transformers/index.html">Transformers</a></h4>
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<ul>
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<li><a href="transformers/mha.html">Multi-headed attention</a></li>
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<li><a href="transformers/models.html">Transformer building blocks</a></li>
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<li><a href="transformers/xl/index.html">Transformer XL</a><ul>
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<li><a href="transformers/xl/relative_mha.html">Relative multi-headed attention</a></li>
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</ul>
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</li>
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<li><a href="transformers/compressive/index.html">Compressive Transformer</a></li>
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<li><a href="transformers/gpt/index.html">GPT Architecture</a></li>
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<li><a href="transformers/glu_variants/simple.html">GLU Variants</a></li>
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<li><a href="transformers/knn/index.html">kNN-LM: Generalization through Memorization</a></li>
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<li><a href="transformers/feedback/index.html">Feedback Transformer</a></li>
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<li><a href="transformers/switch/index.html">Switch Transformer</a></li>
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<li><a href="transformers/fast_weights/index.html">Fast Weights Transformer</a></li>
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<li><a href="transformers/fnet/index.html">FNet</a></li>
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<li><a href="transformers/aft/index.html">Attention Free Transformer</a></li>
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<li><a href="transformers/mlm/index.html">Masked Language Model</a></li>
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<li><a href="transformers/mlp_mixer/index.html">MLP-Mixer: An all-MLP Architecture for Vision</a></li>
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<li><a href="transformers/gmlp/index.html">Pay Attention to MLPs (gMLP)</a></li>
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<li><a href="transformers/vit/index.html">Vision Transformer (ViT)</a></li>
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</ul>
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<h4>✨ <a href="recurrent_highway_networks/index.html">Recurrent Highway Networks</a></h4>
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<h4>✨ <a href="lstm/index.html">LSTM</a></h4>
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<h4>✨ <a href="hypernetworks/hyper_lstm.html">HyperNetworks - HyperLSTM</a></h4>
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<h4>✨ <a href="resnet/index.html">ResNet</a></h4>
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<h4>✨ <a href="capsule_networks/index.html">Capsule Networks</a></h4>
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<h4>✨ <a href="gan/index.html">Generative Adversarial Networks</a></h4>
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<ul>
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<li><a href="gan/original/index.html">Original GAN</a></li>
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<li><a href="gan/dcgan/index.html">GAN with deep convolutional network</a></li>
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<li><a href="gan/cycle_gan/index.html">Cycle GAN</a></li>
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<li><a href="gan/wasserstein/index.html">Wasserstein GAN</a></li>
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<li><a href="gan/wasserstein/gradient_penalty/index.html">Wasserstein GAN with Gradient Penalty</a></li>
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<li><a href="gan/stylegan/index.html">StyleGAN 2</a></li>
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</ul>
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<h4>✨ <a href="sketch_rnn/index.html">Sketch RNN</a></h4>
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<h4>✨ Graph Neural Networks</h4>
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<ul>
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<li><a href="graphs/gat/index.html">Graph Attention Networks (GAT)</a></li>
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<li><a href="graphs/gatv2/index.html">Graph Attention Networks v2 (GATv2)</a></li>
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</ul>
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<h4>✨ <a href="cfr/index.html">Counterfactual Regret Minimization (CFR)</a></h4>
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<p>Solving games with incomplete information such as poker with CFR.</p>
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<ul>
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<li><a href="cfr/kuhn/index.html">Kuhn Poker</a></li>
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</ul>
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<h4>✨ <a href="rl/index.html">Reinforcement Learning</a></h4>
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<ul>
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<li><a href="rl/ppo/index.html">Proximal Policy Optimization</a> with
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<a href="rl/ppo/gae.html">Generalized Advantage Estimation</a></li>
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<li><a href="rl/dqn/index.html">Deep Q Networks</a> with
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with <a href="rl/dqn/model.html">Dueling Network</a>,
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<a href="rl/dqn/replay_buffer.html">Prioritized Replay</a>
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and Double Q Network.</li>
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</ul>
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<h4>✨ <a href="optimizers/index.html">Optimizers</a></h4>
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<ul>
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<li><a href="optimizers/adam.html">Adam</a></li>
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<li><a href="optimizers/amsgrad.html">AMSGrad</a></li>
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<li><a href="optimizers/adam_warmup.html">Adam Optimizer with warmup</a></li>
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<li><a href="optimizers/noam.html">Noam Optimizer</a></li>
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<li><a href="optimizers/radam.html">Rectified Adam Optimizer</a></li>
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<li><a href="optimizers/ada_belief.html">AdaBelief Optimizer</a></li>
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</ul>
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<h4>✨ <a href="https://nn.labml.ai/normalization/index.html">Normalization Layers</a></h4>
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<ul>
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<li><a href="https://nn.labml.ai/normalization/batch_norm/index.html">Batch Normalization</a></li>
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<li><a href="https://nn.labml.ai/normalization/layer_norm/index.html">Layer Normalization</a></li>
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<li><a href="https://nn.labml.ai/normalization/instance_norm/index.html">Instance Normalization</a></li>
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<li><a href="https://nn.labml.ai/normalization/group_norm/index.html">Group Normalization</a></li>
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<li><a href="https://nn.labml.ai/normalization/weight_standardization/index.html">Weight Standardization</a></li>
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<li><a href="https://nn.labml.ai/normalization/batch_channel_norm/index.html">Batch-Channel Normalization</a></li>
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</ul>
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<h4>✨ <a href="distillation/index.html">Distillation</a></h4>
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<h4>✨ <a href="adaptive_computation/index.html">Adaptive Computation</a></h4>
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<ul>
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<li><a href="adaptive_computation/ponder_net/index.html">PonderNet</a></li>
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</ul>
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<h4>✨ <a href="uncertainty/index.html">Uncertainty</a></h4>
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<ul>
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<li><a href="uncertainty/evidence/index.html">Evidential Deep Learning to Quantify Classification Uncertainty</a></li>
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</ul>
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<h3>Installation</h3>
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<pre><code class="bash">pip install labml-nn
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</code></pre>
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<h3>Citing LabML</h3>
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<p>If you use this for academic research, please cite it using the following BibTeX entry.</p>
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<pre><code class="bibtex">@misc{labml,
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author = {Varuna Jayasiri, Nipun Wijerathne},
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title = {labml.ai Annotated Paper Implementations},
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year = {2020},
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url = {https://nn.labml.ai/},
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}
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</code></pre>
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</div>
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<div class="highlight"><pre></pre></div>
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<a href="https://papers.labml.ai">Trending Research Papers</a>
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<a href="https://labml.ai">labml.ai</a>
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