Completed notes of the course AN2DL

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MACHINE LEARNING VS DEEP LEARNING Deep learning has developed in order to analyze data that need to be interpreted by patterns (specific structures). For example, imagine the ECG signal: the doctor analyzes the waves, so the patterns to make a diagnosis, so a prediction. This prediction is based not on each individual failure, but is based on the structure, on the pattern. Deep learning is made to provide predictions over this type of signals. Supervised learning: classification One task is to associate an input to a given class of category. We need to determine if the imagine contains a car or a motorcycle. The input is the image. The target is the type of the content of the image (in this case car or motorcycle). So, the output of our network is a binary. What was typically done to solve this problem is: given an image, extract out some information (features); this is done by smarter program that is taking, for example, color intensity. Supervised learning: regression The input domain is always an image, but the target is not a category, but is the continuous domain, like the price. The problem of regression is to predict how much is the cost of a car or of a motorcycle. The target domain is not a binary, but a real number. What is in common with classification is that, in order to solve these problems, we always need a training set, which are target values associated to images. Unsupervised learning: clustering There are machine learning problems that don’t require to be provided with input and expect an output. There are some figures, and we ask to the machine to group them into distinct categories. So, depending on how are similar, we can identify groups (for example cycles, cars, motorcycles…). This task has to be solved without any supervision. Machine Learning…