This page aims at highlighting a few resources that have been useful for us to put this work together and how to understand Deep Learning a bit better. It is by no means exhaustive, however.

• Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction by Belthangady C. and Royer L.A., in Nature Methods, 2019. A very well-written review on what to expect from Deep Learning and how to look at it from a mathematical point of view. The outlook also highlights some very exciting future applications to microscopy!

• Deep learning for cellular image analysis by Moen E. et al., in Nature Methods, 2019. Another amazing review giving some in-depth insights into the mathematical components of Deep Learning. A great description and explanation of the validation/training error curves is also presented here.

NEUBIAS lectures

• Interactive bioimage analysis with Python and Jupyter PART I
• Introduction to nuclei segmentation with StarDist
• Intro to Machine Learning-DeepLearning-DeepimageJ

Other lectures

• Neocognitron movie. A YouTube link to a 1986 movie about Neural Network, this is quite entertaining!

• 3Blue1Brown playlist on Neural network. A great YouTube video about the mathematical concepts of Neural Networks.

• Stanford Lecture Collection on Convolutional Neural Networks for Visual Recognition. A YouTube series of the 2017 deep learning lecture at Standford University School of Engineering. Really nice to learn more about the history, vocabulary and basic concepts underlying deep learning.