Abstract
Deep learning-based data analysis methods have gained considerable attention in all fields of science over the last decade. In recent years, this trend has reached the single-molecule community. In this review, we will survey significant contributions of the application of deep learning in single-molecule imaging experiments. Additionally, we will describe the historical events that led to the development of modern deep learning methods, summarize the fundamental concepts of deep learning, and highlight the importance of proper data composition for accurate, unbiased results.
Highlights
Deep learning is a class of machine learning techniques that employs artificial neural networks (NNs). [1] The term “learning” indicates in this context that the deep learning algorithm extracts a representation for some analysis task solely from the data with which it is provided
What caused the renaissance of machine learning with NNs? To recall: The approach proposed by Rosenblatt involved a single layer of Perceptrons, each receiving the whole instance of data and directly yielding an output
The capability of NNs to process image data has been especially useful in fields that heavily rely on imaging and microscopy such as medicine, astronomy, and biology
Summary
Deep learning is a class of machine learning techniques that employs artificial neural networks (NNs). [1] The term “learning” indicates in this context that the deep learning algorithm extracts a representation for some analysis task solely from the data with which it is provided. [1] The term “learning” indicates in this context that the deep learning algorithm extracts a representation for some analysis task solely from the data with which it is provided This is possible because NNs are universal function approximators, that is, when the parameters of the algorithm are correct, any continuous function can be represented. One can imagine a function that takes the pixel intensities of the camera image as input and directly returns the parameters of interest Writing down this function analytically is extremely challenging if not impossible, but NNs are an excellent tool to approximate it. The dataset that is set aside at the very beginning of the project, which is used for final testing It is not part of the training set, and, in contrast to the validation set, is not used for hyperparameter optimization and other tasks like that. This dataset was not part of the training set, i.e. the validation set was not used to train the neural network
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