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The main benefit of deep learning is that you don't have to manually design features. Classic Machine Learning algorithms always include the Feature engineering step, whereas neural networks are able to crate features automatically during learning. The classic example is CNN. In the first layer it creates simple features that representing lines, the last ...


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Assume the image can contain objects of class $C_1 \dots C_c$. Assume a set of additional inputs that has a meaning of questions as "contains the image a C_i or C_j or ... ?". The main problem for the system is classify the image in classes $C_i$. Second problem is answer the implicit question proposed by the remainder inputs. Thus, better combine ...


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As you say, the outputs are modeled as a vector, each output in one vector component. In regression problems: The most common loss function, like in the scalar case, is the square error. Skipping constants, it is defined as: $$E=\sum_i ||\mathbf{y_i}-\mathbf{\hat{y_i}}||^2 = \sum_i (\mathbf{y_i}-\mathbf{\hat{y_i}})(\mathbf{y_i}-\mathbf{\hat{y_i}})$$ where: $...


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Assuming you're using softmax on the last layer for classification, it sounds like a simple application of cross entropy loss from here on out: https://datascience.stackexchange.com/questions/20296/cross-entropy-loss-explanation Edit:


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A layer with bigger number of nodes than previous one is something very common. Some examples are: strategies encoder-decoder (autoencoders) where the encoder typically has layers with a decreasing number of nodes (until the compressed/encoded data) and the decoder has layers increasing in number of nodes. bidirectional recurrent networks where in the ...


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