Latin for an Old Man

In high school I dropped the ball when it came to Latin. It was 1964 and one of the 'cool' kids in my all boys, St Aloysius High School brought a pony to Latin class. It galloped around the room and every one of us used it to quickly do our translation homework.  Everyone except Glenn Hymel!

Consequently I didn't learn Latin in high school.  In fact, other than Ola, Adios, si, no, and gracias, I speak only English, and being from New Awlins, even that is barely intelligible.

So, here I am at the age of 75 on a quest to rectify my academic failing.

Since this isn't the first learning quest I've attempted (my previous forays have been short lived, ending when my steed gets entangled on the windmill of enthusiastic lethargy) I'll try to be wise by adopting the following guidelines:

1. Complete 4 chapters a week.
2. Devote no more than 60 minutes to a lesson
3. Do an assessment at the end of each fortnight to adjust guideline 1 and 2

I've reviewed some on-line resources and decided to make my learning goals simple:

1. Complete the 14 Duolingo Latin Lessons
2. Read the 40 chapters in the 6th edition of Wheelock's Latin
3. Do an assessment when goals 1 and 2 are achieved.

Assuming that I can digest 4 chapters each week, it will take me a minimum of 10 weeks to do this.

3 WEEK UPDATE: It has become obvious that 4 chapters a week is too ambitious for me. The book is used for 4 quarters of first year Latin, so one chapter a week is more realistic.  To help me digest these chapters I've found two collection of videos:

1. Wheelock's Latin Chapter Videos by Rebecca Ceferatti
2. Wittenberg Academy: Wheelock's Latin by PastorCurtis

It seems that learning the declensions and conjugations is important so I've backtracked a bit to 'catch up".

The DuoLingo is fun.  It's more user friendly as a web app than a tablet/phone app (there are less advertisements in ther browser version). I may be able to complete the 14 Lessons within 40 days.

GitHub 101.0: cloning a repository on my work machine

(This has been superceded by GitHub 101.1)

Even better than a paragraph a day, a web app project that my son invited me to work on. He started me off with 2 tasks:

1. Make a repository on GitHub
2. Clone the repository to my work machine

So I haven't used GitHub, so first step was auditing the Coursera Introduction to Git and Github.  In this is 16 hour course you will learn to do the following steps. (But, yeah, I didn't take the class as my 'first step'. I went directly to Github, signed up and proceeded to do the steps below.  Then I realized that I really didn't know how Github works. So I'm taking the class : )

Next step, create a New repository by going to the upper right hand corner (if you're just starting you'll receive a prompt to create a repository).

So Task 1 was complete.

Task 2 turned out to be more complicated for me because I didn't have xcode installed.  So, if you are working on a Mac, create your AppleID, and download Xcode. 

I ran into an error when I tried to do the cloning so I had to install the Command Line Tools (reboot your computer after doing the install).

The cloning requires you to sign in from the terminal app and you'll need to create a GitHub personal access token.  

With these precursor steps taken care of, the cloning process was simple. Open the repository you want top clone, go to the "< > Code" drop down and get a copy of the HTTPS url.

Open the terminal window on your computer and enter:

$ git clone <HTTPS url>

You'll be prompted to enter your GitHub user name. Insert a copy of your personal access token when you're prompted for your password. 

Here is a link to the GitHub Cloning a Repository

a paragraph a day

I’m reading “Will College Pay Off” by Peter Cappelli which leads to an examination of the K-12 schools and how well they are preparing our young for life after high school graduation. The federal government National Assessment of Educational Progress provides an annual report card of how we’re doing as a nation as well as how individual states are doing. It turns out that Massachusetts is doing the best in the country, which begs the question, why are its schools so much better?

In short, Massachusetts uses a strategy of increased funding, paying attention to the under performers, longer school days, higher expectations of teachers, holding graduating students accountable (either an acceptance letter from a college or a job offer on company letterhead before they graduate), and covering the basics (students in vocational schools are expected to write about their daily activities.)

Obviously increasing funding for the Kansas school system is beyond my solitary capabilities so I'll focus on the basics: incremental increase in learning time and writing. Based on this I'll model behavior that I hope my children and their children will adopt: 

Write at least a paragraph every day

In Massachusetts schools they write essays. This is a hard task for most people and therefore counterproductive. What is do-able for me is a 15 minute writing session with the goal of 1 paragraph that I could enter in a journal, on a scrap of paper I put in a file folder, or on a social media app. If I can’t think of something impromptu to write about, here are two websites to get me started:

12 Things to Write About When You’re Fresh Out of Ideas grammarly.com

Interesting Topics to Write About journalbuddies.com

. . . and if I feel like a story:

Top 100 Short Story Ideas thewritepractice.com

Lastly, here is an excerpt from the article about why Massachusetts’ schools are 'so much better':

“I’ve always praised Massachusetts for their work on education, but they never want to be praised. They want to know where they’re weak,” he said. “K-12 education is supposed to prepare people to go to college, and a 78 percent graduation rate in Washington state — even though you thump your chest and feel good about your quality of life — means 22 percent of kids are immediately going to the very tail end of the labor-market queue, to the jobs no one else wants — if they get jobs at all.”

That was precisely the fate of 21 percent of students at Worcester Tech when Sheila Harrity, who had never set foot in a vocational school, took the reins when the new building opened in 2006.

Task No. 1: Double the number of honors courses and open them to all.

“What are you doing to our school?” her teachers shrieked. “These are not academic kids!”

A former college basketball star with a straight-shooting style, Harrity next took a hard look at test results. Forty percent of Worcester Tech students were scoring zeros on the written portion of the MCAS, she said. So Harrity imported teacher-trainers to address this. In every classroom, from home construction to hair-coloring, kids were taught to write essays in response to exam questions.

5 Attachments

Condensed AI Challenge: 2 week progress

The best laid schemes o' Mice an' Men 

Gang aft agley

Robert Burns To a Mouse

Made both more progress and less progress than planned.  I bounced around my plan: Artificial Intelligence (AI) article here, Machine Learning (ML) video there; Linear Algebra course here, Python book there. Also, I used more than my scheduled 10 hours a week (more like 30). The long and short of it is that 2 weeks of study gave me an overview glimpse of Artificial Intelligence (the 'more' progress) and confirmed MIT curriculum: I need to learn Linear Algebra and Python first (the less progress).

What is a determinant?

While slugging away at Linear Algebra I hit a roadblock in Imperial College London's Mathematics for Machine learning (coursera). So I backed up by taking a simpler course: Georgia Tech's Linear Algebra II (Edx) and reading a linear algebra chapter in a college math book. I continue to hit roadblocks in Mathematics for Machine Learning; the current one requires side reading on matrix transforms. 

For Python, the Python in 30 minutes course (it has 2 hours of lectures, go figure), and Python for Beginners book gave me enough to start understanding the code. The courses that I have listed include exercises and projects using Python which will deepen my learning.

Here is my updated plan (Yellow items are Phase I tasks; Blue items have been completed). The Orange items are documentaries that I started watching but don't provide the practical content I'm looking for. The Green item I've read but needs a revisit after I learn Machine Learning concepts better.

Resources for Learning AI, ML, DL
SUBJECT	Internet	YouTube	Book	Online Course
Python	Python for Beginners	Learn Python - Full Course for Beginners [Tutorial]	Python crash course: a hands-on, project-based introduction to programming	Development Programming Languages Python Python from Beginner to Intermediate in 30 min.
	Best Way to Learn Python in 2022 (Free and Paid Python Tutorials)	Python Tutorial - Python Full Course for Beginners	Automate the boring stuff with Python: practical programming for total beginners	Coursera: Python for Everybody Specialization
			Python for Beginners: A Smarter Way to Learn Python in 5 Days
Linear Algebra	What is linear algebra, Chapter 1(LibreTexts:Mathematics Mar 2021)	Essence of Linear Algebra	Linear Algebra and Its Applications by David Lay 5th Edition	Coursera: Mathematics for Machine Learning: Linear Algebra
	A Gentle Introduction to Linear Algebra (MachineLearningMastery Aug 2019)	Essence of Linear Algebra		Edx Linear Algebra II: Matrix Algebra
Artificial Intelligence	Oracle: What is AI?	Artificial Intelligence in 5 minutes	AI for Dummies by John Paul Mueller, Luca Massaron (2021)	Coursera: AI for Everyone (Beginner 12 hrs)
	IBM Cloud Education (June 2020)	Artificial intelligence and algorithms: pros and cons	Life 3.0: Being Human in the Age of Artifiucial Intelligence by Max Tegmarck (2017)	IBM: Introduction to AI (Beginner 11 hrs)
	Stanford: Artificial Intelligence		Superintelligence: Paths, Dangers, Strategies by Nick Bolstrom 2016	edX: AI for Everyone: Master the Basics (Beginner 8 hrs)
Machine Learning	Machine Learning Explained in 3 minutes (2017)	Computer Scientist Explains Machine Learning in 5 Levels of Difficulty	Machine Learning for Dummies by John Paul Mueller, Luca Massaron (2016)	Coursera: Machine Learning for All (Beginner 22hrs)
	MIT: Machine learning, explained by Sara Brown (Apr 2021)	Machine Learning: Living in the Age of AI | A WIRED Film	Machine Learning by Tom M. Mitchell (1997 Internet Archives)	IBM: Machine Learning with Python: A Practical Introduction (30 hrs)
	MathWorks: What Is Machine Learning? How it works, why it matters, and getting started	Machine Learning & Artificial Intelligence: Crash Course Computer Science #34	Machine Learning using Python by U Dinesh Kumar	Stanford: Machine Learning Specialization (3 courses, 3 mns@9 hrs/wk)
Deep Learning	Deep Learning by: IBM Cloud Education (May 2020)	Deep Learning In 5 Minutes	Deep Learning A Visual Approach by Phenix40 (2021 Internet Archives)	Coursera: Introduction to Deep Learning (Intermediate 60Hrs)
	MathWorks: What Is Deep Learning? 3 things you need to know		Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python	edX/IBM: Deep Learning Fundamentals with Keras (20hrs)
	What is Deep Learning? by Jason Brownlee on Aug 2020	Neural Networks (the first 2 videos address deep learning)	Neural Networks and Deep Learning by Michael Nielsen (Dec 2019)	Deep Learning Specialization (5 courses, 5 mns@9 hrs/wk)

Artificial Intelligence Challenge - condensed version

After compiling the Artificial Intelligence Challenge I realized that I'm not interested in completing the whole program. I also want to get a deeper understanding of AI and ML than what reading a couple of 'for Dummies' books will give me. To do this I will have to learn some foundational material: Python and Linear Algebra.

My plan is to do the following Artificial Intelligence Challenge - condensed version::

PHASE 1 (5 weeks @8-10hrs/wk)

1. Python: view the YouTube videos (11hrs)
2. Linear Algebra: read the 2 internet pages and complete the first 8 lessons of Essence of Linear Algebra (4hrs)
3. Artificial Intelligence: read the 3 internet pages (3hrs), watch YouTube videos (1hr), and read AI for dummies (5hrs) - TOTAL 9hrs
4. Machine Language: Read the 3 internet pages (2hrs), watch YouTube videos (2hrs), and read ML for dummies (7hrs) - TOTAL 11hrs
5. Deep Learning: Read the 3 internet pages (1hr), watch YouTube videos (2hrs), and read Deep Learning: A Visual Approach (7hrs) - TOTAL 10hrs

PHASE 2 (7 weeks @8-10hrs/week)

1. Python: read Python Crash Course (10hrs) and complete Udemy: Python from Beginner to Intermediate in (1hr) - TOTAL 11hrs
2. Linear Algebra: complete Coursera: Mathematics for Machine Learning: Linear Algebra (19hrs)
3. Artificial Intelligence: Coursera: AI for Everyone (12hrs)
4. Machine Language: Coursera: Machine Learning for All (22hrs)
5. Deep Learning: edX/IBM: Deep Learning Fundamentals with Keras (20hrs)

Based on this my target completion is the end on 2022. At that time I'll determine if I've achieved the level of understanding I want.

Resources for Learning AI, ML, DL
SUBJECT	Internet	YouTube	Book	Online Course
Python	Python for Beginners	Learn Python - Full Course for Beginners [Tutorial]	Python crash course: a hands-on, project-based introduction to programming	Udemy: Python from Beginner to Intermediate in 30 min.
	Best Way to Learn Python in 2022 (Free and Paid Python Tutorials)	Python Tutorial - Python Full Course for Beginners	Automate the boring stuff with Python: practical programming for total beginners	Coursera: Python for Everybody Specialization
Linear Algebra	What is linear algebra (LibreTexts:Mathematics Mar 2021)	Essence of Linear Algebra	Linear Algebra and Its Applications by David Lay 5th Edition	Mathematics for Machine Learning: Linear Algebra
	A Gentle Introduction to Linear Algebra (MachineLearningMastery Aug 2019)
Artificial Intelligence	Oracle: What is AI?	Artificial Intelligence in 5 minutes	AI for Dummies by John Paul Mueller, Luca Massaron (2021)	Coursera: AI for Everyone (Beginner 12 hrs)
	IBM Cloud Education (June 2020)	Artificial intelligence and algorithms: pros and cons	Life 3.0: Being Human in the Age of Artifiucial Intelligence by Max Tegmarck (2017)	IBM: Introduction to AI (Beginner 11 hrs)
	Stanford: Artificial Intelligence		Superintelligence: Paths, Dangers, Strategies by Nick Bolstrom 2016	edX: AI for Everyone: Master the Basics (Beginner 8 hrs)
Machine Learning	Machine Learning Explained in 3 minutes (2017)	Computer Scientist Explains Machine Learning in 5 Levels of Difficulty	Machine Learning for Dummies by John Paul Mueller, Luca Massaron (2016)	Coursera: Machine Learning for All (Beginner 22hrs)
	MIT: Machine learning, explained by Sara Brown (Apr 2021)	Machine Learning: Living in the Age of AI | A WIRED Film	Machine Learning by Tom M. Mitchell (1997 Internet Archives)	IBM: Machine Learning with Python: A Practical Introduction (30 hrs)
	MathWorks: What Is Machine Learning? How it works, why it matters, and getting started	Machine Learning & Artificial Intelligence: Crash Course Computer Science #34	Machine Learning using Python by U Dinesh Kumar	Stanford: Machine Learning Specialization (3 courses, 3 mns@9 hrs/wk)
Deep Learning	Deep Learning by: IBM Cloud Education (May 2020)	Deep Learning In 5 Minutes	Deep Learning A Visual Approach by Phenix40 (2021 Internet Archives)	Coursera: Introduction to Deep Learning (Intermediate 60Hrs)
	MathWorks: What Is Deep Learning? 3 things you need to know		Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python	edX/IBM: Deep Learning Fundamentals with Keras (20hrs)
	What is Deep Learning? by Jason Brownlee on Aug 2020	Neural Networks (the first 2 videos address deep learning)	Neural Networks and Deep Learning by Michael Nielsen (Dec 2019)	Deep Learning Specialization (5 courses, 5 mns@9 hrs/wk)