Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Aleksandr Timashov
Random Walk in the Age of GNNs: Unveiling Its Continued Relevance and Applications
References . L. Lovász, "Random Walks on Graphs: A Survey," in Combinatorics, Paul Erd?s is Eighty (Volume 2), Keszthely, Hungary, 1993, pp. 1–46. . S. Brin and L. Page, "The anatomy of a large-scale hypertextual web search engine," Computer Science Department, Stanford University, Stanford, CA 94305, USA, sergey@cs.stanford.edu and page@cs.stanford.edu. . "Suppressing random walks in Markov chain Monte Carlo using ordered overrelaxation," 21 June 1995. . E. F.
Overcome Lack of Labels
All successfully implemented machine learning models are backed by at least two strong components: data and model. In my discussions with ML engineers, I heard many times that, instead of spending a significant amount of time on data preparation, including labeling for supervised learning, they would rather spend their time on model development. When it comes to most problems, labeling huge amounts of data is way more difficult than obtaining it in the first place.
Engineering Practices for Machine Learning Lifecycle at Google and Microsoft
As demands for AI applications grow, we’ve seen a lot of effort put by companies to build their Machine Learning Engineering (MLE) tools tailored for their needs. There are just so many challenges faced by industries in regards to having a well-designed environment for their Machine Learning (ML) lifecycle: building, deploying, and managing ML models in production. This post will cover two papers, explaining MLE practices from two of the leading tech companies: Google and Microsoft.
Live CNN Training Dashboard: Hyperparameters Tuning
Why we need to build a Live CNN Training Dashboard? Introduction Prerequisites System description How to create an environment and start training? Conclusion References When I studied in mathematical lyceum, my teacher taught me that the best way to understand something is to visualize it. For example, we had a wooden board, plasticine, and metal wire to be able to visualize stereometry problems. It helped a lot to develop visual thinking and skills in solving challenging tasks.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Aleksandr
Contact Aleksandr, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack