Metadata-Version: 2.1
Name: TearDrop
Version: 0.0.2.dev2
Summary: Python algorithms used to perform machine learning.
Home-page: https://gitlab.com/dec0ded/teardrop
Author: Dec0Ded
Author-email: 4323565-dec0ded@users.noreply.gitlab.com
License: LGPL-3.0-ONLY
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: > 3.7.4
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.18.1)
Requires-Dist: pandas (>=1.0.1)

# TearDrop

TearDrop project contains many useful various machine learning algorithms and models. You can
find there anything from Linear regression, KNN, SVMs up to deep learning and LSTMs. 

## Installation
Installing from pypi using `pip`:
```
pip install teardrop
# or you can do this
python3 -m pip install teardrop
```
You can also install it directly from our [repository](https://gitlab.com/dec0ded/teardrop):
```
pip install git+https://gitlab.com/dec0ded/teardrop
```

## Example code
Using TearDrop you can easily create many various neural nets, e.g. Dense neural network.
```python
from teardrop.layers.core import Dense
from teardrop.neural_models import Sequential

net = Sequential(loss='mse', optimizer='sgd')
net.add(Dense(10, activation='relu', input_shape=5))
net.add(Dense(1, activation='sigmoid'))
```
And voila! We've created a basic network which is able to take inputs with shape `(N, 5)`
and returns output with shape `(N, 1)`.

For more examples and better description, check our [documentation](https://dec0ded.gitlab.io/teardrop).

