https://github.com/eschmidt42/random-neural-net-models

Implementations of a random selection of artificial neural net based models and methods.
https://github.com/eschmidt42/random-neural-net-models

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Implementations of a random selection of artificial neural net based models and methods.

• Host: GitHub
• URL: https://github.com/eschmidt42/random-neural-net-models
• Owner: eschmidt42
• License: bsd-3-clause
• Created: 2023-07-03T07:05:11.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-29T10:14:15.000Z (9 months ago)
• Last Synced: 2024-10-14T13:17:31.817Z (2 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 149 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# random neural nets

Implementations of a random selection of artificial neural net based models and methods.

## Python version

Development is done using [`pyenv`](https://realpython.com/intro-to-pyenv/), pinning the python version to the one in the file `.python-version`.

## Installation (on Linux)

Package + notebooks:

    git clone https://github.com/eschmidt42/random-neural-net-models.git

    cd random-neural-net-models

    make install

Package only:

    pip install random-neural-net-models

## Usage

See jupyter notebooks in [`nbs/`](https://github.com/eschmidt42/random-neural-net-models/tree/main/nbs) for:

* fastai style learner with tensordict: `learner-example.ipynb`

* perceptron: `perceptron.ipynb`

* backpropagation: `backpropagation_rumelhart1986.ipynb`

* convolution: `convolution_lecun1990.ipynb`

* cnn autoencoder:

    * mnist: `cnn_autoencoder_fastai2022.ipynb`

    * fashion mnist: `cnn_autoencoder_fastai2022_fashion.ipynb`

* variational autoencoder:

    * dense: `dense_variational_autoencoder_fastai2022.ipynb`

    * cnn+dense: `cnn_variational_autoencoder_fastai2022.ipynb`

* optimizers: `stochastic_optimization_methods.ipynb`

* resnet: `resnet_fastai2022.ipynb`

* unet:

    * `unet_fastai2022.ipynb`

    * `unet-isbi2012`

* diffusion (unet + noise):

  * `diffusion_fastai2022.ipynb`

  * `diffusion_fastai2022_learner.ipynb`

  * `diffusion_fastai2022_learner_with_attention.ipynb`

* [mingpt](https://github.com/karpathy/minGPT):

    * `mingpt_sort.ipynb`

    * `mingpt_char.ipynb`

    * `mingpt_adder.ipynb`

* transformer: `language-model.ipynb`

* tokenization: `tokenization.ipynb`

* tabular:

  * `tabular-fastai-classification.ipynb`

  * `tabular-fastai-classification-with-missingness.ipynb`

  * `tabular-fastai-classification-with-missingness-and-categories.ipynb`

  * `tabular-fastai-regression.ipynb`

  * `tabular-fastai-regression-with-missingness.ipynb`

  * `tabular-fastai-regression-with-missingness-and-categories.ipynb`

  * `tabular-variational-auto-encoder.ipynb`

  * `reusing-vae-for-classification.ipynb`