Tools

Here is a list of tools that we're using in the following tutorial:

• Homebrew: This is a package manager for macOS. Official site: https://brew.sh/.
• Python: This is a general-purpose programming language popular for machine learning and data science. Official site: https://www.python.org/.
• pip: This is a Python package manager. Unlike CocoaPods, it installs libraries globally, and not in a per-project manner.
• Virtualenv: This is a tool for creating separate Python environments with different Python versions and library sets.
• IPython: This is an interactive Python REPL for scientific computations.
• Jupyter: This is a web-GUI for IPython. Official site: http://jupyter.org/.
• Graphviz: This is an open source tool for graphs visualization. We're using it in this chapter to draw models' inner structures. Official site: http://www.graphviz.org/.

And, the Python packages are as follows:

• scipy: This is a Python-based ecosystem of open source software for mathematics, science, and engineering. Official site: https://www.scipy.org/.
• numpy: This is a numerical library.
• matplotlib: This is a popular plotting library.
• pydotplus: This is a library for tree visualization, a counterpart of Graphviz.
• scikit-learn: This is a popular machine learning library. Official site: http://scikit-learn.org/.
• coremltools: is an Apple package for saving scikit-learn models into Core ML format. Official site: https://pypi.python.org/pypi/coremltools.