Overoptimism in machine learning for fluid-related PDEs
In the world of machine learning for fluid-related partial differential equations (PDEs), overoptimism can lead to misleading results and unrealistic expectations. It is crucial to temper enthusiasm with caution and ensure that models accurately capture the complexities of fluid dynamics.
Ask HN: Machine Learning engineers, how was your interview process when hired?
Curious about what it takes to land a job as a machine learning engineer? Hear firsthand experiences from those who have been through the hiring process. Dive into the intricacies of technical interviews, coding challenges, and more in this enlightening article.
Ask HN: Machine Learning and Deep learning books to recommend?
Looking to dive deep into the world of machine learning and deep learning? Check out these top recommended books as suggested by the Hacker News community. Unlock the potential of cutting-edge technology with these essential reads.
How Good Is Parquet for Wide Tables (Machine Learning Workloads) Really?
In the realm of machine learning, the debate over using Parquet for wide tables rages on. Some praise its efficiency, while others cite its limitations. Let's delve into the world of wide tables and see how Parquet truly measures up.
Machine Learning Without Processor: Emergent Learning in Electronic Metamaterial
In a groundbreaking study, researchers have demonstrated how electronic metamaterial can exhibit emergent learning capabilities without the need for a traditional processor. This novel approach, using machine learning algorithms embedded directly into the material, could revolutionize the future of artificial intelligence.
