Adam: A Method for Stochastic Optimization | 5 Minute Paper Podcast скачать в хорошем качестве

Adam: A Method for Stochastic Optimization | 5 Minute Paper Podcast

📄 Adam: A Method for Stochastic Optimization 👥 Authors: Diederik P. Kingma, Jimmy Ba 📅 Published: 2014 | arXiv:cs.LG 🏷️ Topics: optimization, algorithm, adam, stochastic, based ABSTRACT: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescal... TIMESTAMPS: 00:00 - Introduction 01:09 - Exactly! In machine learning, optimization... 02:23 - Adam steps in to make... 03:25 - So the first moment helps... 04:42 - So, without bias correction, it... 05:45 - Oh, I see it! So,... 07:05 - Lets dive into some of... 08:09 - Moving on to Figure 2.... 09:10 - Now, for Convolutional Neural Networks... 10:17 - So, in summary, Adam is... 11:23 - So, less averaging, more peak... 12:31 - Incredible stuff, Tyson. Thanks for... DISCLAIMER: This video contains AI-generated synthetic voices inspired by public figures. These voices are artificially created and do not represent the real persons. This content is for educational and research purposes only and is not affiliated with, endorsed by, or sponsored by Chuck Nice, Neil deGrasse Tyson, or any associated organizations. #AIResearch #MachineLearning #DeepLearning #ResearchPaper #ComputerVision