Nikolai Janakiev explains the concept of the covariance matrix using a bit of Python and some graphs:

In this article we saw the relationship of the covariance matrix with linear transformation which is an important building block for understanding and using PCA, SVD, the Bayes Classifier, the Mahalanobis distance and other topics in statistics and pattern recognition. I found the covariance matrix to be a helpful cornerstone in the understanding of the many concepts and methods in pattern recognition and statistics.

Many of the matrix identities can be found in The Matrix Cookbook. The relationship between SVD, PCA and the covariance matrix are elegantly shown in this question.

Understanding covariance is critical for a number of statistical techniques, and this is a good way of describing it.

A great benefit of Confluent MQTT Proxy is simplicity for realizing IoT scenarios without the need for a MQTT Broker. You can forward messages directly from the MQTT devices to Kafka via the MQTT Proxy. This reduces efforts and costs significantly. This is a perfect solution if you “just” want to communicate between Kafka and MQTT devices.

If you want to see the other part of the story (integration with sink applications like Elasticsearch / Grafana), please take a look at the Github project “KSQL for streaming IoT data“. This realizes the integration with ElasticSearch and Grafana via Kafka Connect and the Elastic connector.

Check it out and then take a gander at Kai’s GitHub repo.

The authors at Knoyd have a post on exploratory data analysis of a time series data set:

From the plot above we can clearly see that time-series has strong seasonal and trend components. To estimate the trend component we can use a function from the pandas library called

rolling_meanand plot the results. If we want to make the plot more fancy and reusable for another time-series it is a good idea to make a function. We can call this functionplot_moving_average.

The second part of the series promises to use Box-Jenkins to forecast future values.

Atul Harsha gives us a demo on k nearest neighbors in Python:

In order to make any predictions, you have to calculate the distance between the new point and the existing points, as you will be needing k closest points.

In this case for calculating the distance, we will use the Euclidean distance. This is defined as the

square root of the sum of the squared differences between the two arrays of numbersSpecifically, we need only first 4 attributes(features) for distance calculation as the last attribute is a class label. So for one of the approach is to limit the Euclidean distance to a fixed length, thereby ignoring the final dimension.

Check it out.

Our example in the video is a simple Keras network, modified from Keras Model Examples, that creates a simple multi-layer binary classification model with a couple of hidden and dropout layers and respective activation functions. Binary classification is a common machine learning task applied widely to classify images or text into two classes. For example, an image is a cat or dog; or a tweet is positive or negative in sentiment; and whether mail is spam or not spam.

But the point here is not so much to demonstrate a complex neural network model as to show the ease with which you can develop with Keras and TensorFlow, log an MLflow run, and experiment—all within PyCharm on your laptop.

Click through for the video and explanation of the process.

Kyle Weller has an inception moment with Python and SQL Server Machine Learning Services:

While this example is trivial with the Iris dataset, imagine the additional scale, performance, and security capabilities that you now unlocked. You can use any of the latest open source R/Python packages to build Deep Learning and AI applications on large amounts of data in SQL Server. We also offer leading edge, high-performance algorithms in Microsoft’s RevoScaleR and RevoScalePy APIs. Using these with the latest innovations in the open source world allows you to bring unparalleled selection, performance, and scale to your applications.

Normally I see examples come straight from SQL Server or maybe C#, but it’s a bit fun to see one originate in Python on order to execute Python in SQL Server.

Tomaz Kastrun shows how to use `pyodbc`

to interact with a SQL Server database from Pandas:

In the SQL Server Management Studio (SSMS), the ease of using external procedure sp_execute_external_script has been (and still will be) discussed many times. But the reason for this short blog post is the fact that, changing Python environments using Conda package/module management within Microsoft SQL Server (Services), is literally impossible. Scenarios, where you want to build a larger set of modules (packages) but are impossible to be compatible with your SQL Server or Conda, then you would need to set up a new virtual environment and start using Python from there.

Communicating with database to load the data into different python environment should not be a problem. Python Pandas module is an easy way to store dataset in a table-like format, called dataframe. Pandas is very powerful python package for handling data structures and doing data analysis.

Click through for examples of reading and writing data.

The Plotly blog shows how to use Python to build 3D cone plots using Plotly:

This plot uses an explicitly defined vector field. A vector field refers to an assignment of a vector to each point in a subset of space.

In this plot, we visualize a collection of arrows that simply model the wind speed and direction at various levels of the atmosphere.

3-D weather plots can be useful to research scientists to gain a better understanding of the atmospheric profile, such as during the prediction of severe weather events like tornadoes and hurricanes.

Sometimes a 3D plot is the best answer. When it is, this looks like a good solution. H/T R-bloggers

Ahmet Taspinar walks us through creating a recurrent neural network topology using TensorFlow:

As we have also seen in the previous blog posts, our Neural Network consists of a

`tf.Graph()`

and a`tf.Session()`

. The`tf.Graph()`

contains all of the computational steps required for the Neural Network, and the`tf.Session`

is used to execute these steps.The computational steps defined in the

`tf.Graph`

can be divided into four main parts;

We initialize placeholders which are filled with batches of training data during the run.

We define the RNN model and to calculate the output values (logits)

The logits are used to calculate a loss value, which then

is used in an Optimizer to optimize the weights of the RNN.

As a lazy casual, I’ll probably stick with letting Keras do most of the heavy lifting.

Dejan Sarka has a few examples of aggregation in different languages, including SQL, R, and Python:

The query calculates the coefficient of variation (defined as the standard deviation divided the mean) for the following groups, in the order as they are listed in the GROUPING SETS clause:

- Country and education – expression (g.EnglishCountryRegionName, c.EnglishEducation)
- Country only – expression (g.EnglishCountryRegionName)
- Education only – expression (c.EnglishEducation)
- Over all dataset- expression ()
Note also the usage of the GROUPING() function in the query. This function tells you whether the NULL in a cell comes because there were NULLs in the source data and this means a group NULL, or there is a NULL in the cell because this is a hyper aggregate. For example, NULL in the Education column where the value of the GROUPING(Education) equals to 1 indicates that this is aggregated in such a way that education makes no sense in the context, for example aggregated over countries only, or over the whole dataset. I used ordering by NEWID() just to shuffle the results. I executed query multiple times before I got the desired order where all possibilities for the GROUPING() function output were included in the first few rows of the result set. Here is the result.

GROUPING SETS is an underappreciated bit of SQL syntax.

Kevin Feasel

2018-08-07

Data Science, Python