Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores electronic, thermomechanical, and amplifier noise, calibration of amplitude, frequency tracking, and system limits.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Covers the basics of image processing for microscopy, including acquiring, correcting defects, enhancing images, and extracting information.
