Best of Computer Science — June 2024

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   Community Picks·2y

   Understanding Time and Space Complexity in Algorithms | Big-O Notation Explained

   Time and space complexity are critical for understanding the efficiency of algorithms. This post explains these concepts and introduces Big O notation, a mathematical notation that helps describe the limiting behavior of a function as the argument tends towards a specific value or infinity. The post covers basic examples to illustrate how to calculate the time and space complexities for different programs and concludes with a detailed discussion on binary search complexity.

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   KDnuggets·2y

   Go to University from Home with These Online Degrees

   Online degrees offer a flexible and accessible way to pursue higher education. The BSc Computer Science degree from the University of London and Goldsmiths University allows for 100% online learning with various specializations like machine learning, AI, and web development. Similarly, the BSc Data Science & Artificial Intelligence from IIT Guwahati provides a flexible timeline with multiple exit options and focuses on application-based learning. Both degrees aim to prepare students for the evolving tech industry, offering hands-on experience and practical skills.

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   Theo - t3․gg·2y

   My Opinions On Git Almost Got Me Cancelled

   The post discusses the importance of Git and version control for computer science graduates. It argues that after four years of coding, graduates should know at least basic Git commands. The author emphasizes that version control is a critical skill for developers, necessary for managing code efficiently and collaboratively. The post also points out that learning Git is not particularly time-consuming and should be a fundamental part of any developer's skill set.

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   MIT News·2y

   Understanding the visual knowledge of language models

   Language models trained purely on text have a solid understanding of the visual world. They can generate complex scenes and refine their images. Researchers from MIT have tested the visual knowledge of these models and trained a computer vision system without using any visual data directly. The models demonstrate creativity in drawing concepts differently each time and their visual knowledge can be combined with other AI tools for improved results.

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