Press "Enter" to skip to content

Category: Python

Implementing an LSTM Model with Python

Published 2020-09-02 by Kevin Feasel

Mrinal Walia takes us through the concept of Long Short Term Memory:

A simple Recurrent Neural Network has a very simple structure, that forms a chain of repeating modules of a neural network, with just a single activation function such as tanh layer, similarly LSTM too have a chain-like structure with repeating modules just like RNN but instead of a single Neural network layer in RNN, LSTM has four layers which are interacting in a very different way each performing its unique function in the network.

Read on for a good amount of theory followed by an example using Keras.

Comments closed

Generating Predictions with SQL Server ML Services

Published 2020-09-02 by Kevin Feasel

Jeffin Mathew walks us through SQL Server Machine Learning Services:

The purpose of this blog is to explore the process of running ML predictions on SQL server using Python. We are going to train and test the data to predict information about bike sharing for a specific year. We are going to be using the provided 2011 data and predict what 2012 will result in. The 2012 data already exists inside the dataset, so we will be able to compare the predicted to the actual amount.

For certain use cases—especially when the data already exists in SQL Server, and especially especially when you can use native scoring—Machine Learning Services does a great job.

Comments closed

Using Jupyter as an External Tool for Power BI Desktop

Published 2020-08-18 by Kevin Feasel

David Eldersveld continues a series on Power BI external tools:

Many people use Python with notebooks, so let’s take a look at one possible way to enable a Jupyter external tool for Power BI Desktop. The following stepwise approach begins with simply opening Jupyter. It then progresses to creating and opening a notebook that includes Power BI’s server and database arguments. Finally, it works its way toward downloading a notebook definition contained in a GitHub gist and connects to Power BI’s tabular model to start to make this approach more useful.

This post continues a series of posts related to Python and Power BI. The first three parts of this blog series introduced some possible uses for Python connected to a Power BI model, how to setup a basic Python external tool, and how to both use it with a virtual environment and connect to the Tabular Object Model.

This was a cool usage of Power BI’s external tool functionality and starts to give you an idea of how powerful it can be.

Comments closed

Getting Started with Jupyter Notebooks

Published 2020-08-17 by Kevin Feasel

Aveek Das takes us through the most popular name in notebooks:

In this article, I am going to explain what Jupyter Notebooks are and how to install the same on your machine. Further, I will demonstrate how to use these notebooks using Visual Studio Code and perform data analysis and other development activities. It is an open-source platform using which you can create and share documents that contain live code, equations, and visualizations along with the formatted text. Most importantly, these notebooks can be run on the web browser by just starting a server and using it. This open-source project is maintained by the team at Project Jupyter.

This is a fairly basic introduction to the topic, good if you have heard about notebooks but don’t know where to begin.

Comments closed

Methods and Functions in Python

Published 2020-08-13 by Kevin Feasel

Sairam Uppugundla distinguishes methods from functions in Python:

Function and method both look similar as they perform in an almost similar way, but the key difference is the concept of ‘Class and its Object’. Functions can be called only by its name, as it is defined independently. But methods cannot be called by its name only we need to invoke the class by reference of that class in which it is defined, that is, the method is defined within a class and hence they are dependent on that class.

Read on to see how to create each, as well as more details on types of functions.

Comments closed

New Features in Python 3.9

Published 2020-08-12 by Kevin Feasel

Harini Guptha walks us through some of what’s upcoming in Python 3.9:

The latest beta preview python 3.9.0b5 is released on July 20th, 2020. Yes, you heard it right. This is the last of the five planned beta release previews. This beta release gives an opportunity to the community to test the new features.

If you are planning to go for a python certification, make sure that you check out what functionalities are deprecated and what features are newly added in this release. Its always good to get yourself updated with the latest information. Without further ado, let’s go through the new features of Python 3.9.0b5.

Click through for the list.

Comments closed

Using the Azure Architecture Icons

Published 2020-07-28 by Kevin Feasel

Steve Jones tries out some of the Azure Architecture Icons:

The icons are svg, so while they work in PowerPoint, adding them to something like this post in OpenLiveWriter doesn’t work. However, I could make a quick diagram and capture an image of it.

Not great, but it shows I can put icons on a page with arrows.

Going one step further, I’ve been digging into Diagrams by mingrammer lately. With it, you use Python to generate diagrams, and there are quite a few Azure icons in there, as well as AWS, on-prem, etc.

Here’s a quick example of what you can do, taken from an upcoming talk of mine:

There are some limitations based on the underlying library, such as how you can’t connect cluster to cluster—meaning I can’t draw a line from “Logging” to “Storage\Logs”; I have to draw it from a particular element (Loki) to a particular element (Elasticsearch). In a lot of traditional reference architecture diagrams, though, that isn’t a problem.

Comments closed

Python and Power BI Desktop

Published 2020-07-24 by Kevin Feasel

David Eldersveld continues a series on Python as an external tool in Power BI. Part 2 is about defining a proper JSON formatted file:

To see a new tool in the Power BI Desktop ribbon, you need to define a JSON file and place it in a specific folder on your workstation. The featured external tools Tabular Editor, DAX Studio, and ALM Toolkit have installers that take care of this step. Since you do not have a dedicated installer to place this file in the required directory, you need to manually define your own.

As long as the “enhanced metadata format” for the data model is enabled, and the JSON in your file is accurate, you should see your new tool in your ribbon after you re-open Power BI Desktop.

Part 3 shows off virtual environments in Python as well as how to connect to the Tabular Object Model:

The Tabular Object Model (TOM) library for .NET opens Power BI’s data model to external tools. Two pieces are required to allow Python to interface with .NET:
1) Pythonnet package for .NET CLR (pip install pythonnet)
2) Python-SSAS module (ssas_api.py placed in the same folder as the main script you’d like to run)

The python-ssas (ssas_api.py) Python module that facilitates the TOM connection is all the work of Josh Dimarsky–originally for querying and processing Analysis Services. I simply repurposed it for use with Power BI Desktop or the XMLA endpoint in Power BI Premium and extended it with some relevant examples. Everything relies on Josh’s Python module, which has functions to connect to TOM, run DAX queries, etc.

This does look pretty handy.

Comments closed

Python in Power BI Desktop

Published 2020-07-22 by Kevin Feasel

David Eldersveld dives into using Python as an external tool in Power BI:

Why use Python as an external “tool”? Even though Python isn’t a “tool” in the same sense as the “Big 3” community tools focused this month, I want to show how versatile the External Tools feature is. I also want to encourage people to use imagination and also explore how Power BI isn’t really as closed as some people think–at least the data model…

Some of these ideas are not exclusive to Python, but there’s enough variety in the Power BI and data science communities for people to possibly figure out if some of this might be useful within the context of their own environments, skills, and organizations.

David also follows up with a series of sample ideas.

Comments closed

Comparing Gradient Descent to the Normal Equation for Small Data Sets

Published 2020-07-15 by Kevin Feasel

Pushkara Sharma compares two techniques for regression:

In this article, we will see the actual difference between gradient descent and the normal equation in a practical approach. Most of the newbie machine learning enthusiasts learn about gradient descent during the linear regression and move further without even knowing about the most underestimated Normal Equation that is far less complex and provides very good results for small to medium size datasets.

If you are new to machine learning, or not familiar with a normal equation or gradient descent, don’t worry I’ll try my best to explain these in layman’s terms. So, I will start by explaining a little about the regression problem.

I was surprised by the results.

Comments closed