I watched this video over the weekend and wanted to share this very well done presentation by a Venture Capital (VC) firm with you — that’s why I love following VC’s (especially one’s who invest in Data/Analytics theme) since they tend to share some amazing insights on where the industry is going.
“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
Things are clearly progressing rapidly when it comes to machine intelligence. But how did we get here, after not one but multiple “A.I. winters”? What’s the breakthrough? And why is Silicon Valley buzzing about artificial intelligence again?
From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
A machine learning algorithm in Azure ML has few parameter settings that you can set — in this post, we will talk about 1) why you should NOT stick with default settings 2) How can “Tune model hyperparameters” module help you do so?
So first up, why you should not be using the default setting? The parameter settings that are applied to a model impacts the accuracy (or call it predictive power) of the algorithm…sometimes it may be significant and sometimes not but either case, you won’t know until you try changing the default values. In other words, by tuning the hyperparameters you could significantly boost your model’s performance!
Now, how do you go about setting the parameters such that it gives optimal performance? Let’s say that there are 3 parameters then that is 27 different combinations! How do you know which one is the best? You could dig a little deeper into the mechanism of how algorithms works and narrow down your list but that would still take some time. So, there should be a better way, right? Luckily there is: This where “Tune Model Hyperparameters” comes in! You can use it with any algorithm in Azure ML. This module helps you tune the hyper-parameters. There’s some things that you still need do like decide: Do you want the module to just try random n combinations? OR Do you want the module to try all combinations (fyi: this is compute-intensive operation!)? … AND you will have to decide what are you are optimizing for? Depending on the algorithm it would let you pick the evaluation metric that you want to optimize.
Now, there are some good articles already written that walks you through how to get about doing this so I am going to share these links with you:
Running the “Entire Grid” mode will slow down the training time for the model. You might want to make sure that it’s acceptable and the cost (longer training time) to benefit (better accuracy) is worth it for your case
When you are comparing algorithms to decide the best one that fits your problem, instead of comparing “model with default parameter settings” with each other, try comparing the “model with tuned hyper-parameters”
Next week, on Mar 15th, 2016, We at Business Analytics VC are hosting a webinar to help you dive a little bit deeper with azure machine learning and learn about building a model to predict customer churn. Even if you don’t use Azure, I think you can still benefit from learning about the use-cases and the framework to solve this problem. You can register using this URL:
If you’re getting started with Data Science & Machine Learning then I think this would be a great resource for you. This “cheat sheet” helps you select the “algorithm” to test depending on the problem you are trying to solve and the data-set that you have.
This post is a quick summary for all Business Analytics related updates that I saw at PASS Summit’14:
1. Theme of the Keynote(s)/Session(s) seemed to be around educating the community about the benefits of the NEW(er) tools. I saw demos/material for cloud-based tools like SQL databases, Azure stream analytics, Azure DocumentDB, AzureHDInsight & Azure Machine learning. The core message was pretty clear: A data professional does two things – 1) Guards data OR 2) helps to generate Insights from Data – And they will need to keep up-to-date on the new tools to future-proof their career.
4. Azure Machine Learning adds a free-tier! You won’t need a credit-card/subscription to sign up for this.
5. I also saw sessions proposing new way of thinking about an architecture for “Self Service BI” and “Big Data” which might be worth following because since these are newer tools, it’s definitely worth considering an architecture that’s designed to make the most of the investments in these new tools. That’s it & I’ll leave you with a quote from James Phillips from Day 1’s keynote:
Data on its own is just a bucket of potential. People convert data to opportunities – James #sqlpass#summit14
Microsoft has introduced a new technology for developing analytics applications in the cloud. The presenter has an insider’s perspective, having actively provided feedback to the Microsoft team which has been developing this technology over the past 2 years. This session will 1) provide an introduction to the Azure technology including licensing, 2) provide demos of using R version 3 with AzureML, and 3) provide best practices for developing applications with Azure Machine Learning.
Mark is a consultant who provides enterprise data science analytics advice and solutions. He uses Microsoft Azure Machine Learning, Microsoft SQL Server Data Mining, SAS, SPSS, R, and Hadoop (among other tools). He works with Microsoft Business Intelligence (SSAS, SSIS, SSRS, SharePoint, Power BI, .NET). He is a SQL Server MVP and has a research doctorate (PhD) from Georgia Tech.