Materias: “…Artificial intelligence Examinations Study guides…”

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Materias: “…Artificial intelligence Medical applications…”

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Tabla de Contenidos: “…5.5 PyTorch's autograd: Backpropagating all things -- 5.5.1 Computing the gradient automatically -- 5.5.2 Optimizers a la carte -- 5.5.3 Training, validation, and overfitting -- 5.5.4 Autograd nits and switching it off -- 5.6 Conclusion -- 5.7 Exercise -- 5.8 Summary -- 6 Using a neural network to fit the data -- 6.1 Artificial neurons -- 6.1.1 Composing a multilayer network -- 6.1.2 Understanding the error function -- 6.1.3 All we need is activation -- 6.1.4 More activation functions -- 6.1.5 Choosing the best activation function -- 6.1.6 What learning means for a neural network -- 6.2 The PyTorch nn module -- 6.2.1 Using __call__ rather than forward -- 6.2.2 Returning to the linear model -- 6.3 Finally a neural network -- 6.3.1 Replacing the linear model -- 6.3.2 Inspecting the parameters -- 6.3.3 Comparing to the linear model -- 6.4 Conclusion -- 6.5 Exercises -- 6.6 Summary -- 7 Telling birds from airplanes: Learning from images -- 7.1 A dataset of tiny images -- 7.1.1 Downloading CIFAR-10 -- 7.1.2 The Dataset class -- 7.1.3 Dataset transforms -- 7.1.4 Normalizing data -- 7.2 Distinguishing birds from airplanes -- 7.2.1 Building the dataset -- 7.2.2 A fully connected model -- 7.2.3 Output of a classifier -- 7.2.4 Representing the output as probabilities -- 7.2.5 A loss for classifying -- 7.2.6 Training the classifier -- 7.2.7 The limits of going fully connected -- 7.3 Conclusion -- 7.4 Exercises -- 7.5 Summary -- 8 Using convolutions to generalize -- 8.1 The case for convolutions -- 8.1.1 What convolutions do -- 8.2 Convolutions in action -- 8.2.1 Padding the boundary -- 8.2.2 Detecting features with convolutions -- 8.2.3 Looking further with depth and pooling -- 8.2.4 Putting it all together for our network -- 8.3 Subclassing nn.Module -- 8.3.1 Our network as an nn.Module -- 8.3.2 How PyTorch keeps track of parameters and submodules…”

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