Software engineering for data scientists – Part 1, Development Tools

I have bad news; being able to write programs is only a tiny fraction of software development and data science. You have to know a lot of things and this can become very frustrating. You will be bombarded with acronyms and silly-named tools, like IDE, git, version control, CI, etc, etc. At some point, you have to start use the tools of the trade along with coding and here we give you some advice on what to use to become a pro.

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A simple Markov chain text generator in Python

What does it mean to build a language model? How it is related to the Beat Generation’s cut-up technique and can be used for text generation? We’ll shed light on these questions using Python!

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Order emerging from randomness or the joy of random Boolean networks and Python

Boolean networks are widely used in computational biology to model gene regulatory networks, and they can be very useful in time-series analysis too. Here, we will use the concept of random Boolean networks to generate interesting images which show some sort of repetitive patterns. Let complexity meet generative art and Python!

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Getting Started with Colab Using TensorFlow is freely available on Manning LP

We developed a free course for Manning liveProject. Getting Started with Google Colab Using TensorFlow has got a very descriptive title, so it teaches you the very basics of using Colab with TensorFlow.

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Getting Started with Colab Using PyTorch is freely available on Manning LP

We developed a free course for Manning liveProject. Getting Started with Google Colab Using PyTorch has got a very descriptive title, so it teaches you the very basics of using Colab with PyTorch.

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Getting Started with Jupyter is freely available on Manning liveProject

We’ve been working hard on our newest liveProject, Getting Started with Jupyter Notebook during the last few months and now it is freely available!

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Content Analysis with Similarity Graphs – the talk is available on YouTube

We presented our thoughts on content analysis with similarity graphs at the Graph Data Science Conference. The full conference is available on Twitch, and Orsolya’s talk is now available on our YouTube channel. You can watch it below!

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A Diary on Information Theory – a lightweight yet rigorous intro into the field

Information Theory (IT) was born in 1948 when Claude Shannon published its a Mathematical Theory of Communication. This seminal work uses linguistic examples, so Information Theory is related to language since its inception. There were many attempts to explain IT to the general audience, but Rényi’s Diary is outstanding with its unique style, clear prose and rigor. If you read only one book on IT, read this one!

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Content Analysis with Similarity Graphs

We had the opportunity to present our thoughts on content analysis with similarity graphs at the Graph Data Science conference. Below, you can watch our presentation and other useful materials to get started with similarity graphs.

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Content Analysis with Similarity Graphs @ Graph Data Science Conference

You can join us to watch Orsolya’s presentation on Content Analysis with Similarity Graphs at the upcoming live@manning Twitch conference on Graph Data Science.

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Crow Intelligence

Author: crowintelligenceteam

Software engineering for data scientists – Part 1, Development Tools

A simple Markov chain text generator in Python

Order emerging from randomness or the joy of random Boolean networks and Python

Getting Started with Colab Using TensorFlow is freely available on Manning LP

Getting Started with Colab Using PyTorch is freely available on Manning LP

Getting Started with Jupyter is freely available on Manning liveProject

Content Analysis with Similarity Graphs – the talk is available on YouTube

A Diary on Information Theory – a lightweight yet rigorous intro into the field

Content Analysis with Similarity Graphs

Content Analysis with Similarity Graphs @ Graph Data Science Conference