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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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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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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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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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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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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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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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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This tiny little book is a timeless introduction into the wonderful world of Mathematics which contains what constitutes the theoretical minimum. It treats its subject in an entertaining and intuitive way. It doesn’t bother its readers with proofs and technicalities, rather it shows the joyful nature of thinking about mathematical problems.
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Even seasoned data scientists can have a hard time when one asks them what does it mean to build a (mathematical) model of a problem, or how math is able to represent the world around us and how pure and applied mathematics are related to each other. Alfred Rényi’s Dialogues on Mathematics is a beautiful book on those questions and it is written for the general audience.
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