| Name | random-neural-net-models JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | None |
| Summary | My implementation of a random selection of artificial neural net based models. |
| upload_time | 2024-03-29 10:22:02 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | BSD 3-Clause License Copyright (c) 2023, Eric Schmidt Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
neural-networks
machine-learning
deep-learning
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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`
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"description": "# random neural nets\n\nImplementations of a random selection of artificial neural net based models and methods.\n\n## Python version\n\nDevelopment is done using [`pyenv`](https://realpython.com/intro-to-pyenv/), pinning the python version to the one in the file `.python-version`.\n\n## Installation (on Linux)\n\nPackage + notebooks:\n\n git clone https://github.com/eschmidt42/random-neural-net-models.git\n cd random-neural-net-models\n make install\n\nPackage only:\n\n pip install random-neural-net-models\n\n## Usage\n\nSee jupyter notebooks in [`nbs/`](https://github.com/eschmidt42/random-neural-net-models/tree/main/nbs) for:\n* fastai style learner with tensordict: `learner-example.ipynb`\n* perceptron: `perceptron.ipynb`\n* backpropagation: `backpropagation_rumelhart1986.ipynb`\n* convolution: `convolution_lecun1990.ipynb`\n* cnn autoencoder:\n * mnist: `cnn_autoencoder_fastai2022.ipynb`\n * fashion mnist: `cnn_autoencoder_fastai2022_fashion.ipynb`\n* variational autoencoder:\n * dense: `dense_variational_autoencoder_fastai2022.ipynb`\n * cnn+dense: `cnn_variational_autoencoder_fastai2022.ipynb`\n* optimizers: `stochastic_optimization_methods.ipynb`\n* resnet: `resnet_fastai2022.ipynb`\n* unet:\n * `unet_fastai2022.ipynb`\n * `unet-isbi2012`\n* diffusion (unet + noise):\n * `diffusion_fastai2022.ipynb`\n * `diffusion_fastai2022_learner.ipynb`\n * `diffusion_fastai2022_learner_with_attention.ipynb`\n* [mingpt](https://github.com/karpathy/minGPT):\n * `mingpt_sort.ipynb`\n * `mingpt_char.ipynb`\n * `mingpt_adder.ipynb`\n* transformer: `language-model.ipynb`\n* tokenization: `tokenization.ipynb`\n* tabular:\n * `tabular-fastai-classification.ipynb`\n * `tabular-fastai-classification-with-missingness.ipynb`\n * `tabular-fastai-classification-with-missingness-and-categories.ipynb`\n * `tabular-fastai-regression.ipynb`\n * `tabular-fastai-regression-with-missingness.ipynb`\n * `tabular-fastai-regression-with-missingness-and-categories.ipynb`\n * `tabular-variational-auto-encoder.ipynb`\n * `reusing-vae-for-classification.ipynb`\n",
"bugtrack_url": null,
"license": "BSD 3-Clause License Copyright (c) 2023, Eric Schmidt Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ",
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