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Python News Friday, July 6

Comparison of top data science libraries for Python, R and Scala [Infographic]

  • So while many languages can be useful for a data scientist, these three remain the most popular and are developed to implement data science and machine learning solutions.
  • In this post, we have prepared an infographic which shows top 20 libraries in each programming language which are beneficial to data scientists and data engineers work.
  • Although there are many specific fields of application of different data science packages, we want to focus on those that are perfectly suited for machine learning, visualization, mathematics and engineering, data manipulation and analysis, and reproducible research.
  • Therefore, the language has many great libraries for machine learning and engineering; however, it lacks data analysis and visualization possibilities comparing to previous languages.
  • These are the languages and libraries that have proved to be extremely useful in various data science use cases.

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Comparison of top data science libraries for Python, R and Scala [Infographic]

SQL, Python and R in One Platform

  • Mode is the data platform that works the way analysts think.
  • Explore with SQL, Python, or R – – Analysis requires multiple tools.
  • Mode lets you use the language that best suits the job, without jumping between applications.
  • Build custom visualizations, or use our built-in charts – – Mode has built-in charts to create reports and dashboards in seconds.
  • Use the tool that’s most comfortable: D3, ggplot, matplotlib, or Mode’s drag-and-drop charting tools to explore data and communicate what you’ve found.

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SQL, Python and R in One Platform

Qt for Python – Making a QML Application in Python {tutorial}

  • Pretty much everything you can do with Qt, now you can do in Python!
  • Here’s how you can make a QML application with Qt for Python.
  • Learn more about Qt for Python

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Qt for Python – Making a QML Application in Python {tutorial}

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  • I have been brewing the idea of using machine learning to improve software systems since 2016\.
  • I have been brewing the idea of using machine learning to improve software systems since 2016.
  • Most decisions to configure and tune the system are made based on the contextthere are many different variables such as workload, number of instances of some services, resources usage, and more.
  • This led me to think that its not only interesting but natural to think about enhancing software systems with machine learning.
  • We also need to find practical and clean ways to integrate machine learning components into software systems, making learning a first-class citizen in the system.

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Trello

  • Your browser was unable to load all of Trello’s resources.
  • They may have been blocked by your firewall, proxy or browser configuration.Press Ctrl+F5 or Ctrl+Shift+R to have your browser try again and if that doesn’t work, check out our troubleshooting guide

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Trello

Python Weekly – Issue 354 

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Python Weekly – Issue 354 

Dogs vs. Cats: Image Classification with Deep Learning using TensorFlow in Python

  • Given a set of labeled images of catsanddogs, amachine learning modelis to be learnt and later it is to be used to classify a set of new images ascatsordogs.
  • Cats – – As apre-processingstep, all the images are firstresizedto5050pixel images.
  • Clearly, the model learnt aboveoverfitsthe training dataset, thetest accuracyimproved a bit, but still quite poor.
  • Lets use the following conv-net shown in the next figure – – The following animations show thefeatureslearnt at 1100 labeled images (randomly chosen from the training dataset) were used to train the model and predict 1000 test images (randomly chosen from the test dataset).
  • Clearly the accuracy can be improved a lot if a large number of images are used fro training with deeper / more complex networks (with more parameters to learn).

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Dogs vs. Cats: Image Classification with Deep Learning using TensorFlow in Python

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ResearchGate

Deep Learning versus Machine Learning in One Picture

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Deep Learning versus Machine Learning in One Picture

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