Here are my thoughts about the "HTML is not a programming language" recurrent theme.
↓ 1/5
This question is controversial not because people care about HTML but because it is used as a proxy to classify their worth.
If HTML is not a programming language, then the people working with HTML must not be real programmers, right?
↓ 2/5
This is demeaning and completely unhelpful for those who are starting and looking to find a community.
Instead of drawing lines, we should be welcoming those who want to join us. We need more programmers, coders, developers, or whatever else you want to call them!
↓ 3/5
This argument is fueled by insecure people that believe in their unexistent superiority.
There's no special ritual to become a programmer. No specific things you must do or tools you have to use.
Nobody owns the title, and nobody needs to allow you in the club.
↓ 4/5
And what's even funnier: answering whether HTML is a programming language or not is completely uninteresting and inconsequential.
HTML will continue to be the Internet's language, and people will keep making money using it.
5/5
• • •
Missing some Tweet in this thread? You can try to
force a refresh
If you are looking for more, this course from Harvard University is an excellent introduction to probability as a language and a set of tools for understanding statistics, science, risk, and randomness.
A lot in machine learning is pretty dry and boring, but understanding how autoencoders work feels different.
This is a thread about autoencoders, things they can do, and a pretty cool example.
↓ 1/10
Autoencoders are lossy data compression algorithms built using neural networks.
A network encodes (compresses) the original input into an intermediate representation, and another network reverses the process to get the same input back.
↓ 2/10
The encoding process "generalizes" the input data.
I like to think of it as the Summarizer in Chief of the network: its entire job is to represent the entire dataset as compactly as possible, so the decoder can do a decent job at reproducing the original data back.
We usually talk about two main types of machine learning models:
• A Classification model
• A Regression model
They are different, and it's essential to understand why.
↓ 1/6
Whenever the result of your predictions is categorical, you have a classification model.
For example, when your prediction is a binary value (True or False,) or when you want to predict a specific animal from a picture (Lion, Zebra, Horse.)
↓ 2/6
If the result of your predictions is numerical, you have a regression model.
For example, returning a stock's future price, the value of a house, or tomorrow's temperature.