My implementation of a random selection of artificial neural net based models.
Project description
random neural nets
Implementations of a random selection of artificial neural net based models and methods.
Python version
Development is done using 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/
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
- mnist:
- variational autoencoder:
- dense:
dense_variational_autoencoder_fastai2022.ipynb
- cnn+dense:
cnn_variational_autoencoder_fastai2022.ipynb
- dense:
- 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:
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Close
Hashes for random-neural-net-models-0.3.0.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | a8943e96c25222d355ff9f75089eb303d4ba5f2dc7a78e0afd18a293781e4326 |
|
MD5 | 0d0a5e1e9bdd1da31eafc6ad93130c38 |
|
BLAKE2b-256 | 833bc81b9337234de0786bcd248ad85dedd3f9509f021123a028b251455704a9 |
Close
Hashes for random_neural_net_models-0.3.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d028cbc6cacdea4a382ab69f7848688c1a4de4d57eca44cd915a635fa58e7552 |
|
MD5 | 1dd083e66b31c85ab43f736baa2a581c |
|
BLAKE2b-256 | ea624cd80fd94129b7feeca429dbef991b85efa539dc28b3970cd1e661d8c658 |