Hello everyone! I am trying to figure out if a main conference registration gives you access to the expo (companies booth) and the expo talks? Expo are happening on July 6th the same day as tutorials. But for tutorials you need to be registered. Do you also need to have a tutorial registration to be able to visit companies booth? Looked on the icml webpage but couldn’t find anything definitive( I did require a free expo badge when I registered ) Thanks submitted by /u/DazzlingPin3965 [link] [Kommentare]
Results are almost here. Good luck to everyone waiting for the final decision 🙂 submitted by /u/Sea_Muscle_4281 [link] [Kommentare]
Dear Folks, I have created multiple content on Machine Learning(work in progress), and they are free. I am a data scientist and a post grad degree holder in AI/ML. To help the machine learning community with important Machine Learning Concepts, I have created multiple long form videos, and structured topicwise digestible contents structured as playlists for learning. If you go through the first two playlists: Introductory Machine Learning Concepts Probability Foundations: Univariate Models You might find helpful content, I have tried explaining with intuitions, derivations, and this is work in progress. For code implementations, scikit learn website has great content on them as well. In total they have 60+ topicwise videos so far, and I think they have the potential to help folks a lot in starting with concepts, or getting with mathematical concepts, or whether you are preparing for an AI/ML/Data job interviews etc. When I sat for my interviews, I was grilled on my project, but majority of questions from my project tested more on foundational concepts and there know how’s. These are FREE content on youtube, and hope it benefits and helps the ML community. submitted by /u/Negative_War_65 [link] [Kommentare]
as in the title, my goal is to predicting failure and RUL of machine, dataset is timestamp and when machine is failure it will labeled with 1 that only have 56 https://preview.redd.it/plbydmenmm6h1.png?width=1205&format=png&auto=webp&s=2fefe3cc2e3fe554b81c9e0b4012c5345e73ec3f From this data im ditching operating hours and humidity because it didnt show correlation for machine failure, what algorithm or deeplearning suit for it? submitted by /u/False-Seesaw-1899 [link] [Kommentare]
Im moving to my final year of engineering, im panicking scared everything but im confident in myself. I can read papers, understand the code go through the architectures and see them at scale (in my head), while i struggle to interpret all the dimensions and helper functions being coupled, i somehow get by hour an abnormal amount of time spent on it. I dont get what i should be doing next? i aspire to combine encoders for vision, audio and ofc text to build a model. but i dont see how that happens overnight, i wanna know what you all experienced folks did after reading papers. it makes me curious about the implications and applications, how real researchers are working on top of it. somewhat like the Big Bang Theory, where all the scientists just discuss ideas, I wish to reach out to researchers too, leave any suggestions on what would help me stand out among all these AI proposals. submitted by /u/EnchantedHawk [link] [Kommentare]
Hi everyone, I'm an undergraduate student and ML researcher at UC Berkeley. My colleagues and I are working on a project that hopes to fix some of the problems users face with Colab. What are the features you wish it had as an ML professional, researcher, or enthusiast? What're the biggest problems you've faced while using it? Some of the issues that everyone feels (including us) is environment management and kernel persistence. But we would love to hear more from the community. submitted by /u/myplstn [link] [Kommentare]