Tag Archives: Machine Learning
Creating Music by Machine Learning
In Sweden, there’s something called the Allemansrätten, which literally translates to “Everyman’s Right.”
Allemansrätten gives everyone the right to access, trespass, and camp on any land within the immediate vicinity of a residential dwelling.
Imagine a stranger strolling onto your property in the U.S. or U.K., pitching a tent on your lawn, and proclaiming “Allemansrätten!”, as they roast marshmallows over an open fire.
In Sweden, this is legal and they’re allowed to stay for up to 24 hours.
Allemansrätten is a very Nordicnotion of the social contract and it embodies the idea of Trust that is at the root of Swedish culture.
The idea of “public” vs. “private” just doesn’t exist to some extent in Sweden, and thus it is not surprising that Sweden has been a leader in both Music Piracy and Music Innovation. Read More
IoT Anomaly detection – algorithms, techniques and open source implementation
Anomaly detection for IoT is one of the archetypal applications for IoT.
Anomaly detection techniques are also used outside of IoT.
In my teaching at the #universityofoxford – we use anomaly detection as a use case because it brings together many of the intricacies for IoT and also demonstrates the use of multiple machine learning and deeplearning algorithms.
Long term, I am exploring the idea of creating an open source anomaly detector for IoT – both for my students and in general. Read More
Python Crash Course for Machine Learning and Data Science
Not to ML when your problem…
Rule of thumb: Which AI / ML algorithms to apply to business problems
How to know which AI/ ML algorithm to apply to which business problem?
This is a common question
Ajit Jaokar found a good reference for it – Executive’s guide to AI by Mc Kinsey
He summarizes the insights in this post: Read More
The Eighty Five Percent Rule for optimal learning
Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the difficulty of training, on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks. For all of these stochastic gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this ‘Eighty Five Percent Rule’ for artificial neural networks used in AI and biologically plausible neural networks thought to describe animal learning. Read More
Complete Hands-Off Automated Machine Learning
Here’ a proposal for real ‘zero touch’, ‘set-em-and-forget-em’ machine learning from the researchers at Amazon. If you have an environment as fast changing as e-retail and a huge number of models matching buyers and products you could achieve real cost savings and revenue increases by making the refresh cycle faster and more accurate with automation. This capability likely will be coming soon to your favorite AML platform. Read More
Read Amazon’s paper here
Machine Learning Models Mindmap
Machine Learning for Everyone
Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairytales about artificial intelligence, data-science magic, and jobs of the future.
Now there’s a simple introduction for those who always wanted to understand machine learning, explained using real-world problems, practical solutions, simple language, and no high-level theorems. Read More
Rick's Cafe AI 
