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 machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.
Model Tuning and
the Bias-Variance Tradeoff
The goal of modeling is to approximate real-life situations by identifying and encoding patterns in data. Models make mistakes if those patterns are overly simple or overly complex.
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!
Getting up-and-running with Amazon’s new machine learning-enabled camera
tl;dr It’s awesome. Get one.
At the end of 2017, Amazon announced DeepLens, a camera with specialized hardware that allows developers to deploy machine learning and computer vision models to “the edge,” and integrate the data it collects with other AWS services.
On a whim, I put in a one-click order on Prime (devices started shipping just last week); it arrived a couple days later and just hours from unboxing — with one or two minor hiccups — I got it up-and-running and integrated with other AWS services. I’ve been pleasantly surprised, to say the least.