Hilarious Graphs (and Pirates) Prove That Correlation Is Not Causation

When it comes to storytelling, we have a problem. It’s not our fault though – as human beings we are hard-wired from birth to look for patterns and explain why they happen. This problem doesn’t go away when we grow up though, it becomes worse the more intelligent we think we are. We convince ourselves that now we are older, wiser, smarter, that our conclusions are closer to the mark than when we were younger (the faster the wind blows the faster the windmill blades turn, not the other way around). Even really smart people see a pattern and insist on putting an explanation to it, even when they don’t have enough information to reach such a conclusion. They can’t help it. This is the thing about being human. We seek explanation for the events that happen around us. If something defies logic, we try to find a reason why it might make sense. If something doesn’t add up, we make it up.


Non stationary K-armed bandit problem in Python

Recently I described simple K-bandit problem and solution. I also did a little introduction to Reinforcement Learning problem. Today I am still going to focus on the same problem with a little bit more terminology and few different algorithms (or more like few different variants). I am not going to exhaust the topic as it’s pretty broad and well studied but to give myself and you dear reader some overview on it. Let’s begin.


Understanding Learning Rates and How It Improves Performance in Deep Learning

This post is an attempt to document my understanding on the following topic:
•What is the learning rate? What is it’s significance?
•How does one systematically arrive at a good learning rate?
•Why do we change the learning rate during training?
•How do we deal with learning rates when using pretrained model?


Machine learning needs machine teaching

In this episode of the Data Show, I spoke with Mark Hammond, founder and CEO of Bonsai, a startup at the forefront of developing AI systems in industrial settings. While many articles have been written about developments in computer vision, speech recognition, and autonomous vehicles, I’m particularly excited about near-term applications of AI to manufacturing, robotics, and industrial automation. In a recent post, I outlined practical applications of reinforcement learning (RL)—a type of machine learning now being used in AI systems. In particular, I described how companies like Bonsai are applying RL to manufacturing and industrial automation. As researchers explore new approaches for solving RL problems, I expect many of the first applications to be in industrial automation.


A Tour of The Top 10 Algorithms for Machine Learning Newbies

In machine learning, there’s something called the “No Free Lunch” theorem. In a nutshell, it states that no one algorithm works best for every problem, and it’s especially relevant for supervised learning (i.e. predictive modeling). For example, you can’t say that neural networks are always better than decision trees or vice-versa. There are many factors at play, such as the size and structure of your dataset. As a result, you should try many different algorithms for your problem, while using a hold-out “test set” of data to evaluate performance and select the winner. Of course, the algorithms you try must be appropriate for your problem, which is where picking the right machine learning task comes in. As an analogy, if you need to clean your house, you might use a vacuum, a broom, or a mop, but you wouldn’t bust out a shovel and start digging.
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