Recommender systems (or recommendation engines) are useful and interesting pieces of software. I wanted to compare recommender systems to each other but could not find a decent list, so here is the one I created. Please help me keep this post up-to-date by submitting corrections and additions via pull-request, or tweet me @grahamjenson.
If you haven’t heard, universities around the world are offering their courses online for free (or at least partially free). These courses are collectively called MOOCs or Massive Open Online Courses.
In the past six years or so, over 800 universities have created more than 10,000 of these MOOCs. And I’ve been keeping track of these MOOCs the entire time over at Class Central, ever since they rose to prominence.
In the past four months alone, 190 universities have announced 600 such free online courses. I’ve compiled a list of them and categorized them according to the following subjects: Computer Science, Mathematics, Programming, Data Science, Humanities, Social Sciences, Education & Teaching, Health & Medicine, Business, Personal Development, Engineering, Art & Design, and finally Science.
If you have trouble figuring out how to signup for Coursera courses for free, don’t worry — here’s an article on how to do that, too.
Many of these are completely self-paced, so you can start taking them at your convenience.
Imagine you could know the mood of the people on the Internet. Maybe you are not interested in its entirety, but only if people are today happy on your favorite social media platform. After this tutorial, you’ll be equipped to do this. While doing this, you will get a grasp of current advancements of (deep) neural networks and how they can be applied to text.
Reading the mood from text with machine learning is called sentiment analysis, and it is one of the prominent use cases in text classification. This falls into the very active research field of natural language processing (NLP). Other common use cases of text classification include detection of spam, auto tagging of customer queries, and categorization of text into defined topics. So how can you do this?
In this post we delve into a reworking of memcached’s Least Recently Used (LRU) algorithm which was made default when 1.5.0 was released. Most of these features have been available via the “-o modern” switch for years. The 1.5.x series has enabled them all to work in concert to reduce RAM requirements.
When memcached was first deployed, it was typically co-located on backend web servers, using spare RAM and CPU cycles. It was important that it stay light on CPU usage while being fast; otherwise it would affect the performance of the application it was attempting to improve.
Over time, the deployment style has changed. There are frequently fewer dedicated nodes with more RAM, but spare CPU. On top of this web requests can fetch dozens to hundreds of objects at once, with the request latency having a greater overall impact.
This post is focused on the efforts to reduce the number of expired items wasting cache space, general LRU improvements, as well as latency consistency.
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.
What is a map function?
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
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.
In today’s blog post you are going to learn how to perform face recognition in both images and video streams using:
- Deep learning
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!