#go#go_interview_questions#go_practice#golang#golang_interview_questions#golang_practice#hacktoberfest#interview#interview_practice#interview_questions#learn_to_code#learning_resources
You can practice and improve your Go programming skills with an interactive web platform that offers 30 coding challenges ranging from beginner to advanced levels. It provides a live code editor with syntax highlighting, instant test results, and detailed performance analytics to help you write efficient Go code. You can track your progress on leaderboards, compare your solutions with others, and learn from detailed explanations and resources for each challenge. The platform supports easy setup via web UI, GitHub Codespaces, or command line, making it convenient to prepare for Go technical interviews and boost your coding confidence. This helps you master Go concepts and get ready for real job interviews effectively.
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What’s Really Going On in Machine Learning? Some Minimal Models—Stephen Wolfram Writings
https://writings.stephenwolfram.com/2024/08/whats-really-going-on-in-machine-learning-some-minimal-models/
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Meta's second version of segment anything.
https://github.com/facebookresearch/segment-anything-2
They have a nice demo:
https://sam2.metademolab.com/
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I was searching for a tool to visualize computational graphs and ran into this preprint. The hierarchical visualization idea is quite nice.
https://arxiv.org/abs/2212.10774
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Like a dictionary
Kunc, Vladim’ir, and Jivr’i Kl’ema. 2024. “Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.09092.
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I got interested in satellite data last year and played with it a bit. It's fantastic. The spatiotemporal nature of it brings up a lot of interesting questions.
Then I saw this paper today:
Rolf, Esther, Konstantin Klemmer, Caleb Robinson, and Hannah Kerner. 2024. “Mission Critical -- Satellite Data Is a Distinct Modality in Machine Learning.” arXiv [Cs.LG], February. http://arxiv.org/abs/2402.01444.
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Jelassi S, Brandfonbrener D, Kakade SM, Malach E. Repeat after me: Transformers are better than state space models at copying. arXiv [cs.LG]. 2024. Available: http://arxiv.org/abs/2402.01032
Not surprising at all when you have direct access to a long context. But hey, look at this title.