semvec/pypi-packages huggingface dataset:
emailfinderpy 1.0.1
Find and verify work emails.
Keywords: python,email,email finder,work email,company domain,start up


# emailfinderpy
## What is it?
This package can be use to verify emails. On top of that, it can also help you find email address. It might take a while to run the code since this code use brute force and try every single possible email combinations.

If you are interested to contribute, feel free to create a pull requests.

## How to use
Install this package using pip
`pip install emailfinderpy` or `pip3 install emailfinderpy`

```python
import emailfinderpy as em

print(em.verify_email('ssuntoso@gmail.com'))
# True

print(em.verify_multiple_email(['s.suntoso@gmail.com', 'ssuntoso@gmail.com'], 'Sean'))
#   Name                Email   Status
#0  Sean  s.suntoso@gmail.com  Veified
#1  Sean   ssuntoso@gmail.com  Veified

# email_finder(first_name, middle_name, last_name, domain)
print(em.email_finder('Sean', '', 'Suntoso', 'gmail.com'))
#            Name                Email   Status
#0  sean  suntoso   ssuntoso@gmail.com  Veified
#1  sean  suntoso  s.suntoso@gmail.com  Veified
```
gaussianmaster 0.1
GaussianMaster is ready to go!
Requires Python: >=3.5

GaussianMaster is here!

aussianMaster Package is a modest Python3.X package allowing faster and more automated extraction
and manipulation on data from Gaussian calculations. It currently supports extraction form results of
optimalization and modredundant (scan) runs. The detailed description of the contents is presented
on:
https://github.com/panpochec/GaussianMaster

You can contact me directly using lysy.pochec@gmail.com

Have fun!
And may the odds be ever in your favour
photon-ml 0.1.2
ML Framework
Requires Python: >=3.7

# Photon: Machine Learning Framework
A end-to-end Machine Learning ramework that extends the functionality of other frameworks such as TensorFlow & Keras. Photon ML is built to apply neural network and ensemble modeling techniques for deep learning financial algorithms. The framework supports the entire lifecycle of a machine learning project including data preparation, model development, training, monitoring, evaluation and deployment.

**Key Features of Photon ML:**

- Streamlines the development and implementation of end-to-end Machine Learning systems.
- Custom object-oriented API with built-in subclassing of Keras and TensorFlow APIs.
- Built-in custom modules such as Models, Layers, Optimizers and Loss Functions.
- Highly customizable interface to extend built-in modules for specific algorithms/networks.
- Detailed logging and analysis of model parameters to increase interpretability and optimization.
- Works natively with TensorFlow distributed strategies.
- Real-time data preprocessing; dataset splitting, normalization, scaling, aggregation & resampling.
- Custom batching, padding and masking of data.
- Designed to be model/algorithm agnostic and to work natively with container services.
- Natively shares input & output between multiple networks to streamline deep ensemble learning.
- Interface for saving, serializing and loading entire networks including learned & hyper parameters.
- Custom dynamic learning rate scheduling.

---
**Photon ML Examples:** https://github.com/sequenzia/photon_examples

**A Collection of Algorthims/Models designed with Photon ML:** https://github.com/sequenzia/dyson
vmware-nsx 16.2.0
VMware NSX library for OpenStack projects
Requires Python: >=3.6

===================
VMware-NSX package
===================

You have come across the VMware-NSX family of Neutron plugins

External Resources:
-------------------

The homepage for the VMware-NSX project is on Launchpad_.

.. _Launchpad: https://launchpad.net/vmware-nsx

Use this site for asking for help, and filing bugs.
Code is available both git.openstack.org_ and github_.

.. _git.openstack.org: https://git.openstack.org/cgit/openstack/vmware-nsx/tree/
.. _github: https://github.com/openstack/vmware-nsx

For help on usage and hacking of VMware-NSX, please send a message to the
openstack-discuss_ mailing list.

.. _openstack-discuss: mailto:openstack-discuss@lists.openstack.org

For information on how to contribute to VMware-NSX, please see the
contents of the CONTRIBUTING.rst file.