Metadata-Version: 2.1
Name: labml-nn
Version: 0.4.111
Summary: 🧑‍🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, etc. 🧠
Home-page: https://github.com/labmlai/annotated_deep_learning_paper_implementations
Author: Varuna Jayasiri, Nipun Wijerathne
Author-email: vpjayasiri@gmail.com, hnipun@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://nn.labml.ai
Description: [![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai)
        
        # [labml.ai Deep Learning Paper Implementations](https://nn.labml.ai/index.html)
        
        This is a collection of simple PyTorch implementations of
        neural networks and related algorithms.
        These implementations are documented with explanations,
        
        [The website](https://nn.labml.ai/index.html)
        renders these as side-by-side formatted notes.
        We believe these would help you understand these algorithms better.
        
        ![Screenshot](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/images/dqn.png)
        
        We are actively maintaining this repo and adding new 
        implementations almost weekly.
        [![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai) for updates.
        
        ## Modules
        
        #### ✨ [Transformers](https://nn.labml.ai/transformers/index.html)
        
        * [Multi-headed attention](https://nn.labml.ai/transformers/mha.html)
        * [Transformer building blocks](https://nn.labml.ai/transformers/models.html) 
        * [Transformer XL](https://nn.labml.ai/transformers/xl/index.html)
            * [Relative multi-headed attention](https://nn.labml.ai/transformers/xl/relative_mha.html)
        * [Compressive Transformer](https://nn.labml.ai/transformers/compressive/index.html)
        * [GPT Architecture](https://nn.labml.ai/transformers/gpt/index.html)
        * [GLU Variants](https://nn.labml.ai/transformers/glu_variants/simple.html)
        * [kNN-LM: Generalization through Memorization](https://nn.labml.ai/transformers/knn)
        * [Feedback Transformer](https://nn.labml.ai/transformers/feedback/index.html)
        * [Switch Transformer](https://nn.labml.ai/transformers/switch/index.html)
        * [Fast Weights Transformer](https://nn.labml.ai/transformers/fast_weights/index.html)
        * [FNet](https://nn.labml.ai/transformers/fnet/index.html)
        * [Attention Free Transformer](https://nn.labml.ai/transformers/aft/index.html)
        * [Masked Language Model](https://nn.labml.ai/transformers/mlm/index.html)
        * [MLP-Mixer: An all-MLP Architecture for Vision](https://nn.labml.ai/transformers/mlp_mixer/index.html)
        * [Pay Attention to MLPs (gMLP)](https://nn.labml.ai/transformers/gmlp/index.html)
        * [Vision Transformer (ViT)](https://nn.labml.ai/transformers/vit/index.html)
        
        #### ✨ [Recurrent Highway Networks](https://nn.labml.ai/recurrent_highway_networks/index.html)
        
        #### ✨ [LSTM](https://nn.labml.ai/lstm/index.html)
        
        #### ✨ [HyperNetworks - HyperLSTM](https://nn.labml.ai/hypernetworks/hyper_lstm.html)
        
        #### ✨ [ResNet](https://nn.labml.ai/resnet/index.html)
        
        #### ✨ [Capsule Networks](https://nn.labml.ai/capsule_networks/index.html)
        
        #### ✨ [Generative Adversarial Networks](https://nn.labml.ai/gan/index.html)
        * [Original GAN](https://nn.labml.ai/gan/original/index.html)
        * [GAN with deep convolutional network](https://nn.labml.ai/gan/dcgan/index.html)
        * [Cycle GAN](https://nn.labml.ai/gan/cycle_gan/index.html)
        * [Wasserstein GAN](https://nn.labml.ai/gan/wasserstein/index.html)
        * [Wasserstein GAN with Gradient Penalty](https://nn.labml.ai/gan/wasserstein/gradient_penalty/index.html)
        * [StyleGAN 2](https://nn.labml.ai/gan/stylegan/index.html)
        
        #### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html)
        
        #### ✨ Graph Neural Networks
        
        * [Graph Attention Networks (GAT)](https://nn.labml.ai/graphs/gat/index.html)
        * [Graph Attention Networks v2 (GATv2)](https://nn.labml.ai/graphs/gatv2/index.html)
        
        #### ✨ [Counterfactual Regret Minimization (CFR)](https://nn.labml.ai/cfr/index.html)
        
        Solving games with incomplete information such as poker with CFR.
        
        * [Kuhn Poker](https://nn.labml.ai/cfr/kuhn/index.html)
        
        #### ✨ [Reinforcement Learning](https://nn.labml.ai/rl/index.html)
        * [Proximal Policy Optimization](https://nn.labml.ai/rl/ppo/index.html) with
         [Generalized Advantage Estimation](https://nn.labml.ai/rl/ppo/gae.html)
        * [Deep Q Networks](https://nn.labml.ai/rl/dqn/index.html) with
         with [Dueling Network](https://nn.labml.ai/rl/dqn/model.html),
         [Prioritized Replay](https://nn.labml.ai/rl/dqn/replay_buffer.html)
         and Double Q Network.
        
        #### ✨ [Optimizers](https://nn.labml.ai/optimizers/index.html)
        * [Adam](https://nn.labml.ai/optimizers/adam.html)
        * [AMSGrad](https://nn.labml.ai/optimizers/amsgrad.html)
        * [Adam Optimizer with warmup](https://nn.labml.ai/optimizers/adam_warmup.html)
        * [Noam Optimizer](https://nn.labml.ai/optimizers/noam.html)
        * [Rectified Adam Optimizer](https://nn.labml.ai/optimizers/radam.html)
        * [AdaBelief Optimizer](https://nn.labml.ai/optimizers/ada_belief.html)
        
        #### ✨ [Normalization Layers](https://nn.labml.ai/normalization/index.html)
        * [Batch Normalization](https://nn.labml.ai/normalization/batch_norm/index.html)
        * [Layer Normalization](https://nn.labml.ai/normalization/layer_norm/index.html)
        * [Instance Normalization](https://nn.labml.ai/normalization/instance_norm/index.html)
        * [Group Normalization](https://nn.labml.ai/normalization/group_norm/index.html)
        * [Weight Standardization](https://nn.labml.ai/normalization/weight_standardization/index.html)
        * [Batch-Channel Normalization](https://nn.labml.ai/normalization/batch_channel_norm/index.html)
        
        #### ✨ [Distillation](https://nn.labml.ai/distillation/index.html)
        
        #### ✨ [Adaptive Computation](https://nn.labml.ai/adaptive_computation/index.html)
        
        * [PonderNet](https://nn.labml.ai/adaptive_computation/ponder_net/index.html)
        
        #### ✨ [Uncertainty](https://nn.labml.ai/uncertainty/index.html)
        
        * [Evidential Deep Learning to Quantify Classification Uncertainty](https://nn.labml.ai/uncertainty/evidence/index.html)
        
        ### Installation
        
        ```bash
        pip install labml-nn
        ```
        
        ### Citing
        
        If you use this for academic research, please cite it using the following BibTeX entry.
        
        ```bibtex
        @misc{labml,
         author = {Varuna Jayasiri, Nipun Wijerathne},
         title = {labml.ai Annotated Paper Implementations},
         year = {2020},
         url = {https://nn.labml.ai/},
        }
        ```
        
        ### Other Projects
        
        #### [🚀 Trending Research Papers](https://papers.labml.ai/)
        
        This shows the most popular research papers on social media. It also aggregates links to useful resources like paper explanations videos and discussions.
        
        
        #### [🧪 labml.ai/labml](https://github.com/labmlai/labml)
        
        This is a library that let's you onitor deep learning model training and hardware usage from your mobile phone. It also comes with a bunch of other tools to help write deep learning code efficiently.
        
        
Keywords: machine learning
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
