Software tools/services to be used during the sessions and hands-on include:
- Anaconda (https://www.anaconda.com): Installation instructions for various platforms can be found at: https://docs.anaconda.com/anaconda/install
- A number of relevant tools and libraries that we will use can be configured from Anaconda: Python 3, NumPy, SciPy, Matplotlib, Jupyter Notebook, Ipython, Pandas, and Scikit-learn.
- For Deep Learning we will use Keras Tensorflow (https://keras.io) to build and train ANN/CNN models. This can be installed from Anaconda, with ‘conda’ from the command line, the actual command line depends on the platform and version (conda install -c conda-forge keras)
- Onlim Platform (https://app.onlim.com/): Conversational and Knowledge Graph Platform. Accounts can be created https://auth.onlim.com/auth/realms/onlim/login-actions/registration?client_id=onlim&tab_id=gmTCMEh3-6U
- DataGraft (https://datagraft.io): Software as a service that requires sign up for a free (DataGraft platform account at https://datagraft.io/users/sign_up where you will be required to provide a username, email and password to access the website).
- Neo4j (https://neo4j.com): Installation and documentation can be found at https://neo4j.com/developer/get-started.We will use the online sandbox service provided at https://neo4j.com/sandbox, so no installation on local machines is needed for experimenting with Neo4j. Alternatively you can download and install Neo4j Desktop, which provides a convenient way for developers to work with local Neo4j databases (this can be downloaded from https://neo4j.com/download-center/#desktop). We will also use Neo4j Graph Data Science (https://neo4j.com/product/graph-data-science) which comes with Neo4j.
- Docker (https://www.docker.com): An open-source containerization platform that will be used for ML pipelines. Installation instructions can be found at https://docs.docker.com/engine/install.