Deep

Deep Learning with TensorFlow in Python

Classifying the letters with notMNIST dataset Let’s first learn about simple data curation practices, and familiarize ourselves with some of the data that are going to be used for deep learning using tensorflow. The notMNIST dataset to be used with python experiments. This dataset is designed to look like the classic MNIST dataset, while looking a little more like real data: it’s a harder task, and the data is a lot less ‘clean’ than MNIST. Preprocessing First the dataset needs to be downloaded and […]

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Java

Four great machine learning eBooks

Want to learn machine learning? Looking for data science tutorials and guides to help you master your data and produce actionable, game-changing insights? Look no further than this list of machine learning eBooks from the Packt team…. 1. Python Machine Learning Python Machine Learning is today one of the most popular machine learning titles on the market. And it’s not hard to see why – by bridging the gap between theory and practice, the author Sebastian Raschka provides you with an […]

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Astronomy

Some Analysis with Astronomy data (in Python)

The following figure shows the median FITS file computed from the above FITS files using the binapprox algorithm. 2. Cross-matching When investigating astronomical objects, like active galactic nuclei (AGN), astronomers compare data about those objects from different telescopes at different wavelengths. This requires positional cross-matching to find the closest counterpart within a given radius on the sky. In this activity you’ll cross-match two catalogues: one from a radio survey, the AT20G Bright Source Sample (BSS) catalogue and one from an optical survey, the SuperCOSMOS all-sky galaxy catalogue. The BSS catalogue […]

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DVC

Best practices of orchestrating Python and R code in ML projects

Today, data scientists are generally divided among two languages — some prefer R, some prefer Python. I will not try to explain in this article which one is better. Instead of that I will try to find an answer to a question: “What is the best way to integrate both languages in one data science project? What are the best practices?”. Beside git and shell scripting additional tools are developed to facilitate the development of predictive model in a multi-language environments. For […]

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CNN

CNN for Short-Term Stocks Prediction using Tensorflow

In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of neural networks that has successfully been applied to image recognition and analysis. In this project I’ve approached this class of models trying to apply it to stock market prediction, combining stock prices with sentiment analysis. The implementation of the network has been made using TensorFlow, starting from the online tutorial. In this article, I will describe the following steps: dataset creation, CNN training and evaluation of […]

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Based

Recommender Engine – Under The Hood

Many of us are bombarded with various recommendations in our day to day life, be it on e-commerce sites or social media sites. Some of the recommendations look relevant but some create range of emotions in people, varying from confusion to anger. There are basically two types of recommender systems, Content based and Collaborative filtering. Both have their pros and cons depending upon the context in which you want to use them. Content based: In content based recommender systems, keywords […]

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Analysis

Machine Learning with Signal Processing Techniques

Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals. Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals. Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with them. In this blog post, we […]

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Data Science

How to Execute R and Python in SQL Server with Machine Learning Services

Introduction Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services   in SQLServer eliminates the need for data movement. Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database. Easily deploy your R/Python code with SQL stored procedures making them accessible in your ETL processes or to any application. Train and store machine learning models […]

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Analysis

PixieDust Support of Streaming Data

With the rise of IoT devices (Internet of Things), being able to analyze and visualize live streams of data is becoming more and more important. For example, you could have sensors like thermometers in machines or portable medical devices like pacemakers, continuously streaming data to a streaming service like Kafka. PixieDust makes it easier to work with live data inside Jupyter Notebooks by providing simple integration APIs to both the PixieApp and display() framework.   On the visualization level, PixieDust […]

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Analytics

Career Transition Towards Data Science: Planning a Learning Sabbatical

At the time of writing this post, I am nine months into my learning sabbatical. You can read about my journey here: “Career Transition Towards Data Analytics & Science”. Today I will share with you how you can plan your own, unique learning sabbatical, regardless of its scope and duration – anywhere between 1 and 12 months. Let’s get started. Begin with the end in mind If you have ever read Stephen Covey’s “7 Habits of Highly Effective People” you […]

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