The grand aim of all science is to cover the greatest number of empirical facts by logical deduction from the smallest number of hypotheses or axioms.
– Albert Einstein
Quoted in:
- The Master Algorithm
People: Albert Einstein
Stay up to date with Machine Learning go here
GitHub
- Awesome Machine Learning: https://github.com/josephmisiti/awesome-machine-learning
- Awesome TensorFlow: https://github.com/jtoy/awesome-tensorflow/
Hacker News
- Search Newest: https://hn.algolia.com/?query=machine%20learning&sort=byDate&prefix&page=0&dateRange=all&type=story
- Search Most relevant: https://hn.algolia.com/?query=machine%20learning&sort=byPopularity&prefix&page=0&dateRange=all&type=story
Medium
- Search Newest: https://www.reddit.com/search?q=machine+learning&sort=new
- Search Most relevant: https://www.reddit.com/search?q=machine+learning&sort=relevance
- Machine Learning: https://www.reddit.com/r/MachineLearning/
- Machine Learning Questions: https://www.reddit.com/r/MLQuestions/
- Machine Learning Paper Discussions: https://www.reddit.com/r/mlpapers/
Blogs
Helpful Image Commands Matlab/Octave
% Load and display an image
img = imread(‘filename.jpg’)
imshow(img)
% Image size
disp(size(img));
% Image class (data type):
disp(class(img));
% At a given location (row, col):
disp(img(50, 100));
% For an entire row:
disp(img(50, :));
plot(img(50, :));
% A slice of an image
img(101:103, 201:203))
% Crop an image
cropped = img(110:310, 10:160)
Color images
Note: Matlab indexes begin at 1 not 0
% Get the red layer (RGB)
img_red = img(:, :, 1);
% Get the green layer (RGB)
img_green = img(:, :, 2);
% Get the blue layer (RGB)
img_blue = img(:, :, 3);
Image arithmetic
% Add two images
summed = img_1 + img_2
% Average of two images
average = img_1 / 2 + img_2 / 2
% Multiply by a scalar
0.5 * img
% Blend two images
result = 0.75 * img_1 + 0.25 * img_2
% Image difference
diff = img_1 – img_2
diff = img_2 – img_1
% Absolute image difference
imabsdiff(img_1, img_2)
Artificial Intelligence Writers on Medium
| Backchannel | https://backchannel.com/tagged/artificial-intelligence |
Know someone who should be on this list? Tweet @scottontech
See also:
Machine Learning Writers on Medium
|
Adam Geitgey |
https://medium.com/@ageitgey |
Know someone who should be on this list? Tweet @scottontech
See also:
Alphabetical 50 US States & Abbreviations List
An online, alphabetical list of the 50 US State names, postal code (2 letter abbreviations), and AP style abbreviations is convenient to have around. Teachers, students, business people, and developers can find this handy. Make lists in spreadsheets, menus, arrays, drop-down lists or combination boxes.
Alphabetical 50 US States & Abbreviations
| Uppercase Name | Name | Postal Code | Abbreviation |
|---|---|---|---|
| ALABAMA | Alabama | AL | Ala. |
| ALASKA | Alaska | AK | Alaska |
| ARIZONA | Arizona | AZ | Ariz. |
| ARKANSAS | Arkansas | AR | Ark. |
| CALIFORNIA | California | CA | Calif. |
| COLORADO | Colorado | CO | Colo. |
| CONNECTICUT | Connecticut | CT | Conn. |
| DELAWARE | Delaware | DE | Del. |
| FLORIDA | Florida | FL | Fla. |
| GEORGIA | Georgia | GA | Ga. |
| HAWAII | Hawaii | HI | Hawaii |
| IDAHO | Idaho | ID | Idaho |
| ILLINOIS | Illinois | IL | Ill. |
| INDIANA | Indiana | IN | Ind. |
| IOWA | Iowa | IA | Iowa |
| KANSAS | Kansas | KS | Kan. |
| KENTUCKY | Kentucky | KY | Ky. |
| LOUISIANA | Louisiana | LA | La. |
| MAINE | Maine | ME | Maine |
| MARYLAND | Maryland | MD | Md. |
| MASSACHUSETTS | Massachusetts | MA | Mass. |
| MICHIGAN | Michigan | MI | Mich. |
| MINNESOTA | Minnesota | MN | Minn. |
| MISSISSIPPI | Mississippi | MS | Miss. |
| MISSOURI | Missouri | MO | Mo. |
| MONTANA | Montana | MT | Mont. |
| NEBRASKA | Nebraska | NE | Neb. |
| NEVADA | Nevada | NV | Nev. |
| NEW HAMPSHIRE | New Hampshire | NH | N.H. |
| NEW JERSEY | New Jersey | NJ | N.J. |
| NEW MEXICO | New Mexico | NM | N.M. |
| NEW YORK | New York | NY | N.Y. |
| NORTH CAROLINA | North Carolina | NC | N.C. |
| NORTH DAKOTA | North Dakota | ND | N.D. |
| OHIO | Ohio | OH | Ohio |
| OKLAHOMA | Oklahoma | OK | Okla. |
| OREGON | Oregon | OR | Ore. |
| PENNSYLVANIA | Pennsylvania | PA | Pa. |
| RHODE ISLAND | Rhode Island | RI | R.I. |
| SOUTH CAROLINA | South Carolina | SC | S.C. |
| SOUTH DAKOTA | South Dakota | SD | S.D. |
| TENNESSEE | Tennessee | TN | Tenn. |
| TEXAS | Texas | TX | Texas |
| UTAH | Utah | UT | Utah |
| VERMONT | Vermont | VT | Vt. |
| VIRGINIA | Virginia | VA | Va. |
| WASHINGTON | Washington | WA | Wash. |
| WEST VIRGINIA | West Virginia | WV | W.Va. |
| WISCONSIN | Wisconsin | WI | Wis. |
| WYOMING | Wyoming | WY | Wyo. |
Download
 |
This list is available under a Creative Commons Attribution license (CC BY 3.0) |
Download “US States List” sot-us-states.csv – Downloaded 409 times – 2 KB
HS: Machine Learning Driven Programming: A New Programming for a New World
If Google were created from scratch today, much of it would be learned, not coded. Around 10% of Google’s 25,000 developers are proficient in ML; it should be 100% — Jeff Dean
Like the weather, everybody complains about programming, but nobody does anything about it. That’s changing and like an unexpected storm the change comes from an unexpected direction: Machine Learning / Deep Learning.
I know, you are tired of hearing about Deep Learning. Who isn’t by now? But programming has been stuck in a rut for a very long time and it’s time we do something about it.
Lots of silly little programming wars continue to be fought that decide nothing. Functions vs objects; this language vs that language; this public cloud vs that public cloud vs this private cloud vs that ‘fill in the blank’; REST vs unrest; this byte level encoding vs some different one; this framework vs that framework; this methodology vs that methodology; bare metal vs containers vs VMs vs unikernels; monoliths vs microservices vs nanoservices; eventually consistent vs transactional; mutable vs immutable; DevOps vs NoOps vs SysOps; scale-up vs scale-out; centralized vs decentralized; single threaded vs massively parallel; sync vs async. And so on ad infinitum.
It’s all pretty much the same shite different day. We are just creating different ways of calling functions that we humans still have to write. The real power would be in getting a machine to write the functions. And that’s what Machine Learning can do, write functions for us. Machine Learning might just might be some different kind of shite for a different day.
Read the full article: Machine Learning Driven Programming on High Scalability
TNW: Understanding the impact of AI
TL;DR Computer coding will become obsolete and we (humans) should teach our children/re-learn ourselves practical and social skills to become better humans.
Understanding the impact of AI
by Matt Webb – 7/6/16
Steam engines, telegrams and typewriters. All obsolete technologies, but well worthy of preservation in the name of engineering history and art.
Coding will join this list in time, however, where it differs wildly from the afore mentioned examples is it is unlikely to be lovingly preserved for future generations to admire, fiddle with or better still, reactivate.
Its essence will not be reified for one specific reason – it can’t be touched and humans value tactility. It’s our basic instinct. We touch immediately, both inside and outside the womb.
Our Final Invention: People, Quotes, Books Mentioned
Our Final Invention
Artificial Intelligence and the End of the Human Era
by James Barrat
People
Key people mentioned in this book (alphabetical)
- Bostrom, Nick
- Kurzweil, Ray
Books
Other books mention in this book (alphabetical)
- Superintelligence by Nick Bostrom
Quotes
Quotes used in this book
Chapter 13
“Both because of its superior planning ability and because of the technologies it could develop, it is plausible to suppose that the first superintelligence would be very powerful. Quite possibly, it would be unrivalled: it would be able to bring about almost any possi-ble outcome and to thwart any attempt to prevent the implemen-tation of its top goal. It could kill off all other agents, persuade them to change their behavior, or block their attempts at inter-ference. Even a “fettered superintelligence” that was running on an isolated computer, able to interact with the rest of the world only via text interface, might be able to break out of its confine-ment by persuading its handlers to release it. There is even some preliminary experimental evidence that this would be the case.” —Nick Bostrom, Future of Humanity Institute, Oxford University
A lot has been written that Watson works through statistical knowledge rather than “true” understanding. Many readers interpret this to mean that Watson is merely gathering statistics on word sequences. . . . One could just as easily refer to the distributed neurotransmitter concentrations in the human cortex as “statistical information.” Indeed, we resolve ambiguities in much the same way that Watson does by considering the likelihood of different interpretations of a phrase. – Ray Kurzweil
Excel Delete Blank Rows while Preserving Data
You could use Excel’s “Remove Duplicates” feature, but any duplicate data that is supposed to be there will also get blown away.
To assuredly remove blank rows without removing duplicate data (and maintaining the original order), follow these steps:
- Locate or insert a blank column
- Optionally name the column “Order” or leave it blank if there are no headers
- Type the number “1” in the first row of data
- Fill down to the last row of data
- Sort by an actual data field
- Now the blank rows will be grouped together, so delete the blank rows.
- Re-sort by the column of numbers from Smallest to Largest.
- Optionally delete the column of numbers
Need to undo this operation? Here’s how to Insert a Blank Row Between Every Row in Excel
Related how-to’s using the functions mentioned here: