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:
- 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
- diffusion (unet + noise):
diffusion_fastai2022.ipynb
- mingpt:
mingpt_sort.ipynb
mingpt_char.ipynb
mingpt_adder.ipynb
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