semvec/pypi-packages huggingface dataset:
Zyxel-T50-Router 0.0.1
A wrapper for the Zyxel T50 modem
Requires Python: >=3.6

# Zyxel T50
A small wrapper for the Zyxel T50 modem.

It can retrieve basic status of the modem and a list of connected devices.
This is used for a [device tracking integration](https://github.com/home-assistant/core/tree/dev/homeassistant/components/zyxelt50) of [Home Assistant](https://www.home-assistant.io/).

# Simple example
```python
import asyncio
import json
from src.zyxelt50.modem import ZyxelT50Modem


async def my_function():
    router = ZyxelT50Modem('#YOUR ADMIN PASSWORD#')
    await router.connect()

    status = await router.get_connected_devices()
    print(json.dumps(status, indent=4))

asyncio.run(my_function())
```
Natasy 0.0.1
A machine learning engine designed and developed to be both easy to use and source code readable.
Requires Python: >=3.6

A machine learning engine designed and developed to be both easy to use and source code readable. It is a straightforward implementation of different algorithms and techniques of machine learning in Python. You can use it for small projects and/or educational purposes.

Backpropagation is implemented in boring detail such that derivative steps is taken carefully and without any implicit or hidden 
details

Natasy is Arabic word means the skilled scientist, or the clever doctor.


Natasy is intended to be easy to use and intuitive to build your neural network with.
To build you network to classify the will known MNIST dataset:
1. Define your network
```python
my_NN = NeuralNetwork(n_features=400, n_classes=10)
```
2. Build up the layers as you want
```python
my_NN.add_layer(100, activation=Activation.leaky_relu, dropout_keep_prob=1)    
my_NN.add_layer(12, activation=Activation.softmax_stable, output_layer=True)
```
3. Finally, call the optimizer
```python
gd_optimizer = Optimizer(loss='multinomial_cross_entropy', method='adam') # gd-with-momentum gradient-descent rmsprop adam
gd_optimizer.minimize(nn01, epochs=100, mini_batch_size=5000, learning_rate=.1, regularization_parameter=0, dataset=mnist)
```

The following is the complete source code for the example. More examples can be found under Natasy/examples.
idle-cn 1.0
IDLE汉化管理器
Requires Python: >=3.0.0

idle汉化器 开源：https://github.com/YHL12345656/idle_cn
安装后
import idle_cn
按步骤执行。
汉化/解除汉化后，请重启IDLE。
在汉化后，请勿直接pip uninstall!
请先调用模块中un_en_cn()！！！！！！
否则IDLE将无法还原！！！！！！
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