https://about.me/irocha
This post discusses how maps are implemented in Go. It is based on a presentation I gave at the GoCon Spring 2018 conference in Tokyo, Japan.
To understand how a map works, let’s first talk about the idea of the map function. A map function maps one value to another. Given one value, called a key, it will return a second, the value.
map(key) → value
Now, a map isn’t going to be very useful unless we can put some data in the map. We’ll need a function that adds data to the map
insert(map, key, value)
and a function that removes data from the map
delete(map, key)
There are other interesting properties of map implementations like querying if a key is present in the map, but they’re outside the scope of what we’re going to discuss today. Instead we’re just going to focus on these properties of a map; insertion, deletion and mapping keys to values.
https://dave.cheney.net/2018/05/29/how-the-go-runtime-implements-maps-efficiently-without-generics
In today’s blog post you are going to learn how to perform face recognition in both images and video streams using:
As we’ll see, the deep learning-based facial embeddings we’ll be using here today are both (1) highly accurate and (2) capable of being executed in real-time.
To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading!
https://www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/
It has been called a “gem” and “pretty much the coolest thing ever,” and if you have not heard of it, then you are missing out on one of the greatest corners of the Python 3 standard library: itertools.
A handful of excellent resources exist for learning what functions are available in the itertools module. The docs themselves are a great place to start. So is this post.
This notebook covers the basics of probability theory, with Python 3 implementations. (You should have some background in probability and Python.)
In 1814, Pierre-Simon Laplace wrote:
Probability … is thus simply a fraction whose numerator is the number of favorable cases and whose denominator is the number of all the cases possible … when nothing leads us to expect that any one of these cases should occur more than any other.
Pierre-Simon Laplace
1814
Laplace really nailed it, way back then! If you want to untangle a probability problem, all you have to do is be methodical about defining exactly what the cases are, and then careful in counting the number of favorable and total cases. We’ll start being methodical by defining some vocabulary:
4.{1, 2, 3, 4, 5, 6}.{2, 4, 6}.This notebook will develop all these concepts; I also have a second part that covers paradoxes in Probability Theory.
http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb
Summary: I learn best with toy code that I can play with. This tutorial teaches backpropagation via a very simple toy example, a short python implementation.
Edit: Some folks have asked about a followup article, and I’m planning to write one. I’ll tweet it out when it’s complete at @iamtrask. Feel free to follow if you’d be interested in reading it and thanks for all the feedback!
http://iamtrask.github.io/2015/07/12/basic-python-network/?hn2=1
I think for many people out there, Blockchain is this phenomenon, which is hard to get your head around. I started watching videos and reading articles, but for me personally, it wasn’t until I wrote my own simple Blockchain, that I truly understood what it is and the potential applications for it.
The way I think about blockchain is it is an encrypted database that is public. If you were Amazon and you wanted to use the technology to track your stock levels, would using Blockchain make sense? Probably not, since your customers won’t want to expend their resources verifying your blockchain, since they state on their website, ‘Only 1 left!’, anyway.
I’ll leave you to think about future applications. So without further ado, lets set up our 7 functions!
https://www.kdnuggets.com/2018/04/blockchain-explained-7-python-functions.html
