Machine Learning – Page 37

Neural Nets With R And Power BI

Published 2017-07-05 by Kevin Feasel

Leila Etaati continues her series on using neural nets in Power BI:

we are going to predict the concrete strength using neural network. neural network can be used for predict a value or class, or it can be used for predicting multiple items. In this example, we are going to predict a value, that is concrete strength.

I have loaded the data in power bi first, and in “Query Editor” I am going to write some R codes. First we need to do some data transformations. As you can see in the below picture number 2,3 and 4,data is not in a same scale, we need to do some data normalization before applying any machine learning. I am going to write a code for that (Already explained the normalization in post KNN). So to write some R codes, I just click on the R transformation component (number 5).

There’s a lot going on in this demo; check it out.

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Neural Nets On Spark

Published 2017-06-28 by Kevin Feasel

Nisha Muktewar and Seth Hendrickson show how to use Deeplearning4j to build deep learning models on Hadoop and Spark:

Modern convolutional networks can have several hundred million parameters. One of the top-performing neural networks in the Large Scale Visual Recognition Challenge (also known as “ImageNet”), has 140 million parameters to train! These networks not only take a lot of compute and storage resources (even with a cluster of GPUs, they can take weeks to train), but also require a lot of data. With only 30000 images, it is not practical to train such a complex model on Caltech-256 as there are not enough examples to adequately learn so many parameters. Instead, it is better to employ a method called transfer learning, which involves taking a pre-trained model and repurposing it for other use cases. Transfer learning can also greatly reduce the computational burden and remove the need for large swaths of specialized compute resources like GPUs.

It is possible to repurpose these models because convolutional neural networks tend to learn very general features when trained on image datasets, and this type of feature learning is often useful on other image datasets. For example, a network trained on ImageNet is likely to have learned how to recognize shapes, facial features, patterns, text, and so on, which will no doubt be useful for the Caltech-256 dataset.

This is a longer post, but on an extremely interesting topic.

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Running H2O In R On Azure HDInsight

Published 2017-06-28 by Kevin Feasel

Daisy Deng shows how to configure HDInsight to be able to run the H2O package in R rather than Python or Scala:

We provide a few script actions for installing rsparkling on Azure HDInsight. When creating the HDInsight cluster, you can run the following script action for header node:

https://bostoncaqs.blob.core.windows.net/scriptaction/scriptaction-head.sh

And run the following action for the worker node:

https://bostoncaqs.blob.core.windows.net/scriptaction/scriptaction-worker.sh

Please consult Customize Linux-based HDInsight clusters using Script Action for more details.

Click through for the full process.

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Basics Of Neural Nets

Published 2017-06-27 by Kevin Feasel

Leila Etaati has a new series on neural nets in R:

in Neural Network, we have some hidden Nodes that do the main job ! they found the best value for the output, they are using some function that we call that functions as “Activation function” for instance in below picture, Node C is a hidden node that take the values from node A and B. as you can see the weight (the better path) related to Node B as shown in tick line that means Node B may lead to get better results so Node C get input values from Node B not Node A.

If you have time, also check out the linked YouTube videos.

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Envisioning Neural Nets As Org Charts

Published 2017-06-27 by Kevin Feasel

Maiia Bakhova describes the layout of a neural net as similar to a chain of command within an organization:

We can observe a lot of in common with a corporation chain of command. As we see middle managers are hidden layers which do the balk of the job. We have the similar information flow and processing which is analogous to forward propagation and backward propagation.

What is left now is to explain that dealing with sigmoid function at each node is too costly so it mostly reserved for CEO level.

That’s a metaphor I hadn’t heard before.

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Spark And H2O

Published 2017-06-23 by Kevin Feasel

Avkash Chauhan shows how to use sparklyr and rsparkling to tie Spark together with the H2O library in R:

In order to work with Spark H2O using rsparkling and sparklyr in R, you must first ensure that you have both sparklyr and rsparkling installed.

Once you’ve done that, you can check out the working script, the code for testing the Spark context, and the code for launching H2O Flow. All of this information can be found below.

It’s a short post, but it does show how to kick off a job.

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Microsoft ML For Park

Published 2017-06-09 by Kevin Feasel

Xiaoyong Zhu announces that the Microsoft Machine Learning library is now available for Spark:

We’ve learned a lot by working with customers using SparkML, both internal and external to Microsoft. Customers have found Spark to be a powerful platform for building scalable ML models. However, they struggle with low-level APIs, for example to index strings, assemble feature vectors and coerce data into a layout expected by machine learning algorithms. Microsoft Machine Learning for Apache Spark (MMLSpark) simplifies many of these common tasks for building models in PySpark, making you more productive and letting you focus on the data science.

The library provides simplified consistent APIs for handling different types of data such as text or categoricals. Consider, for example, a DataFrame that contains strings and numeric values from the Adult Census Income dataset, where “income” is the prediction target.

It’s an open source project as well, so that barrier to entry is lowered significantly.

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Riddler Nation: Game Theory In Action

Published 2017-06-07 by Kevin Feasel

Curtis Miller goes over a multi-phase distribution game with no known information:

The winning strategy of the last round, submitted by Vince Vatter, was (0, 1, 2, 16, 21, 3, 2, 1, 32, 22), with an official record¹ of 751 wins, 175 losses, and 5 ties. Naturally, the top-performing strategies look similar. This should not be surprising; winning strategies exploit common vulnerabilities among submissions.

I’ve downloaded the submitted strategies for the second round (I already have the first round’s strategies). Lets load them in and start analyzing them.

This is a great blog post, which looks at using evolutionary algorithms to evolve a winning strategy.

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Sentiment Analysis In R

Published 2017-06-07 by Kevin Feasel

Stefan Feuerriegel and Nicolas Pröllochs have a new package in CRAN:

Our package “SentimentAnalysis” performs a sentiment analysis of textual contents in R. This implementation utilizes various existing dictionaries, such as QDAP or Loughran-McDonald. Furthermore, it can also create customized dictionaries. The latter uses LASSO regularization as a statistical approach to select relevant terms based on an exogenous response variable.

I’m not sure how it stacks up to external services, but it’s another option available to us.

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Using OtterTune To Tune Databases

Published 2017-06-05 by Kevin Feasel

Dana Van Aken, Geoff Gordon, and Any Pavlo show off OtterTune, which uses machine learning techniques to tune database management systems like MySQL and Postgres:

OtterTune, a new tool that’s being developed by students and researchers in the Carnegie Mellon Database Group, can automatically find good settings for a DBMS’s configuration knobs. The goal is to make it easier for anyone to deploy a DBMS, even those without any expertise in database administration.

OtterTune differs from other DBMS configuration tools because it leverages knowledge gained from tuning previous DBMS deployments to tune new ones. This significantly reduces the amount of time and resources needed to tune a new DBMS deployment. To do this, OtterTune maintains a repository of tuning data collected from previous tuning sessions. It uses this data to build machine learning (ML) models that capture how the DBMS responds to different configurations. OtterTune uses these models to guide experimentation for new applications, recommending settings that improve a target objective (for example, reducing latency or improving throughput).

In this post, we discuss each of the components in OtterTune’s ML pipeline, and show how they interact with each other to tune a DBMS’s configuration. Then, we evaluate OtterTune’s tuning efficacy on MySQL and Postgres by comparing the performance of its best configuration with configurations selected by database administrators (DBAs) and other automatic tuning tools.

This is potentially a very interesting technology and is not the only one of its kind—we’ve seen Microsoft enter this space as well for SQL Server index and tuning recommendations.

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Category: Machine Learning