Please see instructions in the zip

The following Lab items should be carried out by you using your regular Operating System (Windows, macOS, or perhaps Linux) and a browser such as Chrome, Firefox, or Safari.
The purpose of this Lab Exploration Component is to give you more practice with Python 3 & Jupyter and then to introduce you to the Julia language — remember that this is one of the languages as the basis for Jupyter.
Open up the area after each question / section in this MS Word file and type in (or paste) your answer(s) and your comments. Why do I ask you to open up the area and type into this document? You are going to be doing a few hours of work for this Practical. I would like you to keep your work, including the context of the questions asked. For long-term-learning it always a good idea to document what you do and keep it long term. After all, that is the reason for Notebooks in the first place. Thus, document your work and keep it. (That also applies to all the course materials that I give you in this course.)
First Part
Using an existing Python 3 Jupyter Notebook. You are not expected to be an expert in Python 3, but, hopefully, you have an introductory background.
Steps:
Please study these two notebooks and then move on to next steps:
Labeled Faces in the Wild recognition.ipynb
ML-Exercise1.ipynb
__2.    Work your way through these two Python-based Notebooks and cut-and-paste some snippets here to illustrate that you worked them and achieved completion of the work in the notebooks themselves.
__3.    Comment here on what you learned both about the topics chosen and the Jupyter Notebook approach to documenting work (at least a half dozen sentences):
In this exercise we implemented a simple linear regression using gradient descent and...