This repository consists of plug and play notebooks -- allowing first exposure to some tasks in machine learning, natural language processing.

We use pre-trained off-the-shelf models to allow people to interact, enjoy, and play with machine learning. Meanwhile, the tasks expose some well known ML capabilities.

The end goal is to assemble the necessary pieces for explaining the IBM Deep Search.

The jupyter notebooks for each tasks is in the notebooks directory.

• Task 1: Object Detection

• Task 2: Word Embeddings

• Task 3: Named Entity Recognition

python
jupyter-notebook
tensorflow
tensorflow-hub
numpy
pandas
gensim
spacy
matplotlib
PIL

Worry not! Everything is prepared via a docker container that contains all the libraries and packages we need.

This repository is developed and maintained by Scalable Knowledge Ingestion research group at IBM Research, Zurich.

To contribute code or documentation, please submit a pull request.