Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Explores smart, connected products and their transformative impact on companies, covering artificial intelligence, machine learning, predictive models, forecasting methods, and more.
