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Annotated Deep Learning Paper Implementations

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提交 791cd122 编写于 作者: V Varuna Jayasiri
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labml_nn/__init__.py
浏览文件 @ 791cd122
......@@ -2,7 +2,7 @@
[![PiPy Version](https://badge.fury.io/py/labml-nn.svg)](https://badge.fury.io/py/labml-nn)
[![PiPy Downloads](https://pepy.tech/badge/labml-nn)](https://pepy.tech/project/labml-nn)
# [LabML Neural Networks](http://lab-ml.com/labml_nn/index.html)
# [LabML Neural Networks](https://lab-ml.com/labml_nn/index.html)
This is a collection of simple PyTorch implementation of various
neural network architectures and layers.
......@@ -10,35 +10,35 @@ We will keep adding to this.
## Modules
#### ✨ [Transformers](http://lab-ml.com/labml_nn/transformers)
#### ✨ [Transformers](https://lab-ml.com/labml_nn/transformers)
[Transformers module](http://lab-ml.com/labml_nn/transformers)
[Transformers module](https://lab-ml.com/labml_nn/transformers)
contains implementations for
[multi-headed attention](http://lab-ml.com/labml_nn/transformers/mha.html)
[multi-headed attention](https://lab-ml.com/labml_nn/transformers/mha.html)
and
[relative multi-headed attention](http://lab-ml.com/labml_nn/transformers/relative_mha.html).
[relative multi-headed attention](https://lab-ml.com/labml_nn/transformers/relative_mha.html).
* [kNN-LM: Generalization through Memorization](http://lab-ml.com/labml_nn/transformers/knn)
* [kNN-LM: Generalization through Memorization](https://lab-ml.com/labml_nn/transformers/knn)
#### ✨ [Recurrent Highway Networks](http://lab-ml.com/labml_nn/recurrent_highway_networks)
#### ✨ [Recurrent Highway Networks](https://lab-ml.com/labml_nn/recurrent_highway_networks)
#### ✨ [LSTM](http://lab-ml.com/labml_nn/lstm)
#### ✨ [LSTM](https://lab-ml.com/labml_nn/lstm)
#### ✨ [Capsule Networks](http://lab-ml.com/labml_nn/capsule_networks/)
#### ✨ [Capsule Networks](https://lab-ml.com/labml_nn/capsule_networks/)
#### ✨ [Generative Adversarial Networks](http://lab-ml.com/labml_nn/gan/)
* [GAN with a multi-layer perceptron](http://lab-ml.com/labml_nn/gan/simple_mnist_experiment.html)
* [GAN with deep convolutional network](http://lab-ml.com/labml_nn/gan/dcgan.html)
* [Cycle GAN](http://lab-ml.com/labml_nn/gan/cycle_gan.html)
#### ✨ [Generative Adversarial Networks](https://lab-ml.com/labml_nn/gan/)
* [GAN with a multi-layer perceptron](https://lab-ml.com/labml_nn/gan/simple_mnist_experiment.html)
* [GAN with deep convolutional network](https://lab-ml.com/labml_nn/gan/dcgan.html)
* [Cycle GAN](https://lab-ml.com/labml_nn/gan/cycle_gan.html)
#### ✨ [Sketch RNN](http://lab-ml.com/labml_nn/sketch_rnn/)
#### ✨ [Sketch RNN](https://lab-ml.com/labml_nn/sketch_rnn/)
#### ✨ [Reinforcement Learning](http://lab-ml.com/labml_nn/rl/)
* [Proximal Policy Optimization](http://lab-ml.com/labml_nn/rl/ppo/) with
[Generalized Advantage Estimation](http://lab-ml.com/labml_nn/rl/ppo/gae.html)
* [Deep Q Networks](http://lab-ml.com/labml_nn/rl/dqn/) with
with [Dueling Network](http://lab-ml.com/labml_nn/rl/dqn/model.html),
[Prioritized Replay](http://lab-ml.com/labml_nn/rl/dqn/replay_buffer.html)
#### ✨ [Reinforcement Learning](https://lab-ml.com/labml_nn/rl/)
* [Proximal Policy Optimization](https://lab-ml.com/labml_nn/rl/ppo/) with
[Generalized Advantage Estimation](https://lab-ml.com/labml_nn/rl/ppo/gae.html)
* [Deep Q Networks](https://lab-ml.com/labml_nn/rl/dqn/) with
with [Dueling Network](https://lab-ml.com/labml_nn/rl/dqn/model.html),
[Prioritized Replay](https://lab-ml.com/labml_nn/rl/dqn/replay_buffer.html)
and Double Q Network.
### Installation
......
readme.md
浏览文件 @ 791cd122
[![PiPy Version](https://badge.fury.io/py/labml-nn.svg)](https://badge.fury.io/py/labml-nn)
[![PiPy Downloads](https://pepy.tech/badge/labml-nn)](https://pepy.tech/project/labml-nn)
# [LabML Neural Networks](http://lab-ml.com/labml_nn/index.html)
# [LabML Neural Networks](https://lab-ml.com/labml_nn/index.html)
This is a collection of simple PyTorch implementation of various
neural network architectures and layers.
......@@ -9,35 +9,35 @@ We will keep adding to this.
## Modules
#### ✨ [Transformers](http://lab-ml.com/labml_nn/transformers)
#### ✨ [Transformers](https://lab-ml.com/labml_nn/transformers)
[Transformers module](http://lab-ml.com/labml_nn/transformers)
[Transformers module](https://lab-ml.com/labml_nn/transformers)
contains implementations for
[multi-headed attention](http://lab-ml.com/labml_nn/transformers/mha.html)
[multi-headed attention](https://lab-ml.com/labml_nn/transformers/mha.html)
and
[relative multi-headed attention](http://lab-ml.com/labml_nn/transformers/relative_mha.html).
[relative multi-headed attention](https://lab-ml.com/labml_nn/transformers/relative_mha.html).
* [kNN-LM: Generalization through Memorization](http://lab-ml.com/labml_nn/transformers/knn)
* [kNN-LM: Generalization through Memorization](https://lab-ml.com/labml_nn/transformers/knn)
#### ✨ [Recurrent Highway Networks](http://lab-ml.com/labml_nn/recurrent_highway_networks)
#### ✨ [Recurrent Highway Networks](https://lab-ml.com/labml_nn/recurrent_highway_networks)
#### ✨ [LSTM](http://lab-ml.com/labml_nn/lstm)
#### ✨ [LSTM](https://lab-ml.com/labml_nn/lstm)
#### ✨ [Capsule Networks](http://lab-ml.com/labml_nn/capsule_networks/)
#### ✨ [Capsule Networks](https://lab-ml.com/labml_nn/capsule_networks/)
#### ✨ [Generative Adversarial Networks](http://lab-ml.com/labml_nn/gan/)
* [GAN with a multi-layer perceptron](http://lab-ml.com/labml_nn/gan/simple_mnist_experiment.html)
* [GAN with deep convolutional network](http://lab-ml.com/labml_nn/gan/dcgan.html)
* [Cycle GAN](http://lab-ml.com/labml_nn/gan/cycle_gan.html)
#### ✨ [Generative Adversarial Networks](https://lab-ml.com/labml_nn/gan/)
* [GAN with a multi-layer perceptron](https://lab-ml.com/labml_nn/gan/simple_mnist_experiment.html)
* [GAN with deep convolutional network](https://lab-ml.com/labml_nn/gan/dcgan.html)
* [Cycle GAN](https://lab-ml.com/labml_nn/gan/cycle_gan.html)
#### ✨ [Sketch RNN](http://lab-ml.com/labml_nn/sketch_rnn/)
#### ✨ [Sketch RNN](https://lab-ml.com/labml_nn/sketch_rnn/)
#### ✨ [Reinforcement Learning](http://lab-ml.com/labml_nn/rl/)
* [Proximal Policy Optimization](http://lab-ml.com/labml_nn/rl/ppo/) with
[Generalized Advantage Estimation](http://lab-ml.com/labml_nn/rl/ppo/gae.html)
* [Deep Q Networks](http://lab-ml.com/labml_nn/rl/dqn/) with
with [Dueling Network](http://lab-ml.com/labml_nn/rl/dqn/model.html),
[Prioritized Replay](http://lab-ml.com/labml_nn/rl/dqn/replay_buffer.html)
#### ✨ [Reinforcement Learning](https://lab-ml.com/labml_nn/rl/)
* [Proximal Policy Optimization](https://lab-ml.com/labml_nn/rl/ppo/) with
[Generalized Advantage Estimation](https://lab-ml.com/labml_nn/rl/ppo/gae.html)
* [Deep Q Networks](https://lab-ml.com/labml_nn/rl/dqn/) with
with [Dueling Network](https://lab-ml.com/labml_nn/rl/dqn/model.html),
[Prioritized Replay](https://lab-ml.com/labml_nn/rl/dqn/replay_buffer.html)
and Double Q Network.
### Installation
......
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