Here’s a riddle:
Here’s a riddle:
Every semester, Machine Learning at Berkeley takes on new projects. Here’s the latest scoop on the newest projects we’ve taken on this semester.
Neural networks are perhaps one of the most exciting recent developments in machine learning. Got a problem? Just throw a neural net at it. Want to make a self-driving car? Throw a neural net at it. Want to fly a helicopter? Throw a neural net at it. Curious about the digestive cycles of your sheep? Heck, throw a neural net at it. This extremely powerful algorithm holds much promise (but can also be a bit overhyped). In this article we’ll go through how a neural network actually works, and in a future article we’ll discuss some of the limitations of these seemingly magical tools.
Perceptrons, Logistic Regression, and SVMs
In this post we’ll talk about one of the most fundamental machine learning algorithms: the perceptron algorithm. This algorithm forms the basis for many modern day ML algorithms, most notably neural networks. In addition, we’ll discuss the perceptron algorithm’s cousin, logistic regression. And then we’ll conclude with an introduction to SVMs, or support vector machines, which are perhaps one of the most flexible algorithms used today.
One crucial element that all statistical learning algorithms need is the ability to handle a tremendous amount of data very quickly. People have used different frameworks for querying, or fetching, data. Among these include Hadoop’s MapReduce framework and the Apache Spark framework. SAP Hana Vora’s (HV) unique in-memory Hadoop query engine for MapReduce frameworks is a promising new tool for big data and performing analysis in a distributed fashion on large databases of information. We demonstrate HV’s potential as a powerful resource for ML by examining its performance on tasks such as stock prediction on market data. We also contributed some additional functionality to the SAP HV library in the process.
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