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Category: Machine Learning

Creating An Azure Chat Bot

Published 2017-12-14 by Kevin Feasel

Dustin Ryan shows how to build a QnA bot:

After you’ve created your knowledge base you can then edit and update your knowledge base. There’s a few different ways to update your knowledge.

a. Manually edit the knowledge base directly within QnAMaker.ai. You can do this by directly editing the questions by modifying the text of your knowledge base.

b. Edit the source of your knowledge base. Click the Settings tab on the left to edit the URL of your FAQs or upload a new document.

Building a bot is pretty easy, and Dustin shows you just how to do it.

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Estimating Used Car Prices

Published 2017-12-07 by Kevin Feasel

Kevin Jacobs wants to estimate the value of his car and shows how to set up a machine learning job to do this:

As you can see, I collected the brand (Peugeot 106), the type (1.0, 1.1, …), the color of the car (black, blue, …) the construction year of the car, the odometer of the car (which is the distance in kilometers (km) traveled with the car at this point in space and time), the ask price of the car (in Euro’s), the days until the MOT (Ministry of Transport test, a required periodical check-up of your car) and the horse power (HP) of the car. Feel free to use your own variables/units!

It’s an interesting example of how you can approach a real problem.

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Introduction To Neural Nets

Published 2017-12-07 by Kevin Feasel

Ben Gorman has a two-part series introducing neural networks.  First, the basics behind neural networks:

We can solve both of the above issues by adding an extra layer to our perceptron model. We’ll construct a number of base models like the one above, but then we’ll feed the output of each base model as input into another perceptron. This model is in fact a vanilla neural network. Let’s see how it might work on some examples.

Then, he digs into the mathematics of backpropagation:

Our problem is one of binary classification. That means our network could have a single output node that predicts the probability that an incoming image represents stairs. However, we’ll choose to interpret the problem as a multi-class classification problem – one where our output layer has two nodes that represent “probability of stairs” and “probability of something else”. This is unnecessary, but it will give us insight into how we could extend task for more classes. In the future, we may want to classify {“stairs pattern”, “floor pattern”, “ceiling pattern”, or “something else”}.

Our measure of success might be something like accuracy rate, but to implement backpropagation (the fitting procedure) we need to choose a convenient, differentiable loss function like cross entropy. We’ll touch on this more, below.

This is definitely a series to read after you’ve gotten your coffee.

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Training Convolutional Neural Networks On Satellite Image Data

Published 2017-12-06 by Kevin Feasel

Ahmet Taspinar builds a neural net which detects roads in satellite images:

Next we will determine the contents of each tile image, using data from the NWB Wegvakken (version September 2017). This is a file containing all of the roads of the Netherlands, which gets updated frequently. It is possible to download it in the form of a shapefile from this location.
Shapefiles contain shapes with geospatial data and are normally opened with GIS software like ArcGIS or QGIS. It is also possible to open it within Python, by using the pyshp library.

This is a pretty lengthy and interesting tutorial.  H/T Data Science Central

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Predicting Restaurant Reservations With A Neural Net

Published 2017-12-01 by Kevin Feasel

Kevin Jacobs builds a simple neural net using Pandas and sklearn:

The first thing to notice is that our values are not normalized. The number of visitors is a number and gets larger and larger. To normalize it, we simply divide it by 100, since all numbers are below 1. The same holds for the lag. Most of the lags are lower than 30. Therefore, I will divide the lag size by 30.

Notice that there are many more approaches for normalizing the data! This is just a quick normalization on the data, but feel free to use your own normalization method. My normalization process is closely related to the MinMaxScalar normalization which can be found in sklearn (scikit-learn).

With just a few lines of Python code we can create a Multi-Layer Perceptron (MLP):

Click through for the code.

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Using Keras To Predict Customer Churn

Published 2017-11-30 by Kevin Feasel

Matt Dancho has an example of building a neural net using Keras to predict customer churn:

Pro Tip: A quick test is to see if the log transformation increases the magnitude of the correlation between “TotalCharges” and “Churn”. We’ll use a few dplyr operations along with the corrr package to perform a quick correlation.

  • correlate(): Performs tidy correlations on numeric data

  • focus(): Similar to select(). Takes columns and focuses on only the rows/columns of importance.

  • fashion(): Makes the formatting aesthetically easier to read.

This is a very useful tutorial.

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Tidy Word Vectors Revisited

Published 2017-11-29 by Kevin Feasel

Julia Silge revisits her Hacker News word vectorization problem:

So hooray! We have found word vectors again, a bit faster, with clearer and easier-to-understand code. I do argue that this is a real benefit of this approach; it’s based on counting, dividing, and matrix decomposition and is thus much easier to understand and implement than anything with a neural network. And the results?

Click through to see the new method, as well as some basic analogy testing.

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Machine Learning Data Preparation Tips

Published 2017-11-29 by Kevin Feasel

Jen Underwood has some good tips when preparing data for a machine learning operation:

Data preparation for machine learning requires business domain expertise, bias awareness and an experimental thought process. Before preparing your data, you’ll first define a business problem solve. During that exercise, you’ll select an outcome metric and brainstorm potential input variables that influence it from many varied perspectives. From there you will begin identifying, collecting, cleaning, shaping and sampling data to run through automated machine learning model processes.

Note that it is also not unusual for relevant machine learning input data to occur outside of existing transactional processes. If that is the case, you can still start creating a first-generation machine learning model with existing data and continue to build new model versions over time as supplementary data is acquired.

Click through for the ten tips.

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Installing SQL Server 2017 Machine Learning Services

Published 2017-11-29 by Kevin Feasel

Ginger Grant shows how to install SQL Server 2017 Machine Learning Services:

There are two installation options:  In-Database or Standalone.  If you are evaluating Machine Learning Services and you have no knowledge of what the load may be, start by selecting the Machine Learning Service In-Database.  There are several reasons why by default you want to select the In-Database option. One of the problems that Microsoft was looking to solve by incorporating advanced data analytics was to improve performance of the native code by greatly reducing data latency.  If you are analyzing a lot of data which is stored within SQL Server, the performance will be improved if the data does not need to be moved around on a network. Also, the licensing costs of installing R Server standalone also need to be evaluated with a Microsoft representative as well. An evaluation of the resource load on the network, as well as analysis of the code running on SQL Server should be performed prior to the decision to install the Machine Learning Server Standalone.

Read the whole thing.

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