Machine learning toolkit for natural language processing. Written for Lisbon Machine Learning Summer School (lxmls.it.pt). This covers
- Scientific Python and Mathematical background
- Linear Classifiers (Gradient Descent)
- Non-linear Models (Backpropagation)
- Sequence models in deep learning
- Attention Models (Transformers)
Machine learning toolkit for natural language processing. Written for LxMLS - Lisbon Machine Learning Summer School
- This is student branch. You are on the correct branch!
If you are new to Python, the simplest method is to use Anaconda
to handle your packages, just go to
https://www.anaconda.com/download/
and follow the instructions for installation using Python 3. After setting up the anaconda:
use your favorite git tool to create a clone of this repository
navigate to the folder where the repository resides
install anaconda (see instruction)
conda create --name lxmls_new
conda activate lxmls_new
conda install pip
pip install --editable .
If you prefer pip
to Anaconda you can install the toolkit in a way that does
not interfere with your existing installation. For this you can use a virtual
environment as follows
virtualenv venv
source venv/bin/activate (on Windows: .\venv\Scripts\activate)
pip install pip setuptools --upgrade
pip install --editable .
This will install the toolkit in a way that is modifiable. If you want to also
virtualize you Python version (e.g. you are stuck with Python2 on your system),
have a look at pyenv
.
Bear in mind that the main purpose of the toolkit is educative. You may resort to other toolboxes if you are looking for efficient implementations of the algorithms described.
- Run from the project root directory. If an importing error occurs, try first adding the current path to the
PYTHONPATH
environment variable, e.g.:export PYTHONPATH=.