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<a href='#section-0'>#</a>
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<h1><a href="https://lab-ml.com/labml_nn/index.html">LabML Neural Networks</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/lab-ml/nn">These implementations</a> are documented with explanations,
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and the <a href="https://lab-ml.com/labml_nn/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="https://lab-ml.com/labml_nn/transformers">Transformers</a></h4>
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<p><a href="https://lab-ml.com/labml_nn/transformers">Transformers module</a>
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contains implementations for
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<a href="https://lab-ml.com/labml_nn/transformers/mha.html">multi-headed attention</a>
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and
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<a href="https://lab-ml.com/labml_nn/transformers/relative_mha.html">relative multi-headed attention</a>.</p>
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<ul>
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<li><a href="https://lab-ml.com/labml_nn/transformers/gpt">GPT Architecture</a></li>
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<li><a href="https://lab-ml.com/labml_nn/transformers/knn">kNN-LM: Generalization through Memorization</a></li>
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<li><a href="https://lab-ml.com/labml_nn/transformers/feedback">Feedback Transformer</a></li>
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<li><a href="https://lab-ml.com/labml_nn/transformers/switch">Switch Transformer</a></li>
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</ul>
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<h4>✨ <a href="https://lab-ml.com/labml_nn/recurrent_highway_networks">Recurrent Highway Networks</a></h4>
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<h4>✨ <a href="https://lab-ml.com/labml_nn/lstm">LSTM</a></h4>
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<h4>✨ <a href="https://lab-ml.com/labml_nn/hypernetworks/hyper_lstm.html">HyperNetworks - HyperLSTM</a></h4>
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<h4>✨ <a href="https://lab-ml.com/labml_nn/capsule_networks/">Capsule Networks</a></h4>
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<h4>✨ <a href="https://lab-ml.com/labml_nn/gan/">Generative Adversarial Networks</a></h4>
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<ul>
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<li><a href="https://lab-ml.com/labml_nn/gan/simple_mnist_experiment.html">GAN with a multi-layer perceptron</a></li>
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<li><a href="https://lab-ml.com/labml_nn/gan/dcgan.html">GAN with deep convolutional network</a></li>
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<li><a href="https://lab-ml.com/labml_nn/gan/cycle_gan.html">Cycle GAN</a></li>
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</ul>
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<h4>✨ <a href="https://lab-ml.com/labml_nn/sketch_rnn/">Sketch RNN</a></h4>
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<h4>✨ <a href="https://lab-ml.com/labml_nn/rl/">Reinforcement Learning</a></h4>
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<ul>
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<li><a href="https://lab-ml.com/labml_nn/rl/ppo/">Proximal Policy Optimization</a> with
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<a href="https://lab-ml.com/labml_nn/rl/ppo/gae.html">Generalized Advantage Estimation</a></li>
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<li><a href="https://lab-ml.com/labml_nn/rl/dqn/">Deep Q Networks</a> with
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with <a href="https://lab-ml.com/labml_nn/rl/dqn/model.html">Dueling Network</a>,
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<a href="https://lab-ml.com/labml_nn/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="https://lab-ml.com/labml_nn/optimizers/">Optimizers</a></h4>
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<ul>
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<li><a href="https://lab-ml.com/labml_nn/optimizers/adam.html">Adam</a></li>
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<li><a href="https://lab-ml.com/labml_nn/optimizers/amsgrad.html">AMSGrad</a></li>
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<li><a href="https://lab-ml.com/labml_nn/optimizers/adam_warmup.html">Adam Optimizer with warmup</a></li>
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<li><a href="https://lab-ml.com/labml_nn/optimizers/noam.html">Noam Optimizer</a></li>
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<li><a href="https://lab-ml.com/labml_nn/optimizers/radam.html">Rectified Adam Optimizer</a></li>
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<li><a href="https://lab-ml.com/labml_nn/optimizers/ada_belief.html">AdaBelief Optimizer</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 LabML for academic research, please cite the library 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: A library to organize machine learning experiments},
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year = {2020},
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url = {https://lab-ml.com/},
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}
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</code></pre>
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