What are /r/cs7646_fall2017's favorite Products & Services?

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Thanks! Depending on the age of your kids, his book about high school may be more helpful. He also has a second book for college kids about the overall experience. I haven't read either but I'm a big fan of his thinking in general. http://calnewport.com/books/

For everyone else reading, his book So Good They Can't Ignore You has shaped how I think about my career, and I usually buy a copy for people who are trying to figure out which career they want and how to get there.

I'm not entirely sure what you are asking. github.gatech.edu is the server. You can login via a browser to use the webapp. There, you can create a private repo. After creating the repo, you can use git on your local or remote machine to interact with the repository. There are quite a few tutorials out there: udacity, github, etc

Careful with this Python practice question:

import pandas as pd import numpy as np

j = [8, 7, 6, 5, 4, 3, 2, 1] print ([x/j[-2] for x in j[1:-1]])

The output depends on the version of Python.

I know that we're not working with Python version 3.x, but if you're used to using 3.x in your 9-to-5 job, this will output a different answer than will be expected for our exam:

https://repl.it/MQUd/0

If you are interested in this, I highly recommend getting the book Hedge Fund Market Wizards by Jack Schwager and reading the in-depth interview with Jamie Mai in Chapter 7, in which Jamie describes in detail some of his trades. In a nutshell, Jamie put on trades that provided asymmetric payoffs (think deep out of the money options, but structured much more intelligently) that took advantage of pricing inefficiencies to move the reward/risk ratio in his favor.

If you install anaconda, you don't need to worry about any version of python, nympy, and pandas. It will install all the libraries you need. 1) In your vm, open browser and connect to https://www.anaconda.com/download/ and download the correct version of anaconda. 2) https://docs.continuum.io/anaconda/install/ you can find the installation instruction in the link above. Select correct machine you want to use and follow the instruction. It is fairly easy.

I installed it in my Ubuntu 17 and it works fine.

I'm more of a fan of https://cryptowat.ch/ it has a bunch of exchanges and the detail view has all kinds of awesome features ( bollinger bands, volume and more as well as an order book) It's owned by kraken so you can do trades in the interface via that exchange if you don't mind their current lack of security, these charts for me make it worth it. (above disclaimer applies here)

I wonder professor how would a pull make the local changes public if someone is not pushing the commits. You can always restrict the push access by making it read only repository.

https://git-scm.com/docs/git-pull

I highly recommend setting up an upstream repository to the main class git repo, and keeping your origin pointed to your private repo.

This way you can periodically merge updates from the instructor-provided repo into yours, using git fetch upstream && git merge upstream/master

Check out: https://git-scm.com/book/en/v2/Git-Basics-Working-with-Remotes

I agree with nearly everything except for the preparation piece.

My advice: Don't bother with the lectures until the semester begins. Instead, read this book cover-to-cover.

Why? For two reasons:

1. It's a much lighter introduction to Machine Learning! When the semester begins, you'll need to fasten your seatbelt and put your nose to the grindstone. So over the winter break, I recommend that you read this book for preparation.
2. Although Professor Isbell will indeed tell you that you're submitted code is worth zero points, you will need to either write or steal some code to perform your machine learning experiments. While it's true that you can use Python or even Java for some of the assignments, R is a perfect language to complete half of the assignments.

In conclusion, you'll have a huge advantage coming into the course if you've read this Lantz book cover-to-cover.

You can write the code in whichever language you like. In fact, Professor Isbell repeatedly says, "You can steal the code; he doesn't care, because you are awarded precisely zero points for your code." You are only graded on your analysis.

I chose R for three reasons:

1. I didn't know Python at the time.
2. Someone on the OMSCS Google+ channel recommending learning R before taking the ML course.
3. I learned a lot about ML and R by reading a terrific book: https://www.amazon.com/Machine-Learning-Second-Brett-Lantz/dp/1784393908