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
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-0.1.1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7426ae5c6eeef6197846454c036dfd2b90ec502c27f637f9682ce62e25713964 |
|
MD5 | 2ec9ec76b08666c9d366fe858fc17728 |
|
BLAKE2b-256 | 9ddcf47eae1003d0c9ef25d090ed378f7a5db00692e1a37845330a38dadd3b88 |
Close
Hashes for random_neural_net_models-0.1.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2771a453d58040195906b4c244f7b45bd07f3026b9027f4c30c59f8324163eb0 |
|
MD5 | b187654427324872594fc5e2d7405b36 |
|
BLAKE2b-256 | 317e6ec2c6451b360a623e7429632faf764c1cc43c74c35f7928a6a4e93c15f6 |