🎥 Recorded video: Lecture 3
🗓 May 12, 6pm
Description
Contents: extended review of previous material, training a 2D function (reproducing an arbitrary image), first steps towards analyzing and interpreting a neural network, influence of batch size and learning rate, brief introduction to the keras package and its basic neural network routines, image recognition (one-hot-encoding, softmax, cross-entropy), the danger of overfitting, training vs. validation vs. test data
After this lecture, you will have more experience in training, understand the dangers, and know what the most elementary hyperparameters such as batch size or learning rate will do. You will also have a first taste of Keras, the convenient high-level neural-network software package (included in every TensorFlow installation). You will know the basics of how image recognition works, which is one of the most important applications of neural networks. But you don’t yet know about the more advanced network structures one can build, such as the convolutional neural networks important for image recognition.