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approach to software decomposition whereby fundamental requirements on types are abstracted from across concrete examples of algorithms and data structures and formalized as concepts, analogously to the abstraction of algebraic theories in abstract
algebra. Early examples of this programming
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Herz nach Maß The biases and weights in the Network object are all initialized randomly, using the Numpy np.random.randn function to generate Gaussian distributions with mean $0$ and standard deviation $
1$. This random initialization gives our stochastic gradient descent algorithm a place to start from. In later chapters we'll find better ways of initializing the weights and biases, but this will do for now. The Canadian Mathematical Society (CMS) has entered into a publishing partnership with Cambridge University Press (Cambridge). The web site for the
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Mathematics. Notes on prerequisites. Unless stated otherwise, the minimum grade acceptable in all course prerequisites is a C-. In exceptional cases, course prerequisites may be waived by an instructor.
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