Table of Contents
This page is dedicated to a 2-days course on Neural network with application to natural language processing (NNet for NLP). The drive with all the ressources is here.
1 Expected road-map
1.1 First day : 01/07, from 9h to 17h (with breaks)
Courses on:
- From logistic regression to NNet
- Machine learning basics
- NNet for NLP
And lab sessions on:
- Pytorch basics,
- Logistic regression
- Text classification
1.2 Second day : 02/07, from 9h to 17h (with breaks)
Courses on:
- Sequence models
- Neural Machine translation
- ELMo and BERT
And lab sessions on:
- Convolution for text classification
- Sequence tagging
2 NNet basics
To introduce neural networks, start with the videos from Hugo Larochelle. The roadmap is
- Capsules 1.1 to 1.6 : the artificial neuron and the feed-forward architecture (definition)
- Capsules 2.1 to 2.11: training basics
3 Pytorch basics
Download this notebook and run it with jupyter. To get the tools you can install anaconda3 and then pytorch.
PyTorch is python module. If you need a python refresher:
- https://learnpythonthehardway.org/python3/
- A good free book Dive in python that covers the basics and essentials.
- Otherwise (in French) the wiki site Apprendre à programmer en python.
- And of course don't forget to take a look to the standard documentation in French or in in English
- More practicing: https://github.com/gregmalcolm/python_koans
Numpy is one of the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.
- A quick introduction to python and numpy and matplotlib: http://cs231n.github.io/python-numpy-tutorial/
- With Numpy exercises: https://github.com/rougier/numpy-100
- If you are already familiar with MATLAB: http://scipy.github.io/old-wiki/pages/NumPy_for_Matlab_Users
- And for R users : http://mathesaurus.sourceforge.net/r-numpy.html
Using python can be easier with ipython, look at this tutorial: http://cs231n.github.io/ipython-tutorial/. If you like more standard IDE : https://www.jetbrains.com/pycharm-edu/