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Title[redir]:
The Fundamentals of Autograd — PyTorch Tutorials 2.7.0+cu126 documentation
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Keywords {🔍}
tensor, autograd, function, pytorch, gradients, computation, inputs, model, requiresgradtrue, output, learning, loss, input, tensors, models, vector, weights, derivatives, gradient, lets, training, history, object, jacobian, outputs, derivative, computed, compute, call, docs, respect, printc, xffea, def, printmodellayerweightgrad, time, torchones, torchrand, cuda, cpu, total, api, tutorials, partial, computing, note, graph, backward, method, return,
Topics {✒️}
/var/lib/workspace/beginner_source/introyt/autogradyt_tutorial cpu time total cuda time total high-level api takes m-dimensional input github introduction web site terms single-valued scalar function plt import matplotlib pytorch foundation supports fine-grained control including performance notes make pytorch flexible pytorch functions meant repeated doubling operation pytorch model carries m-dimensional output depth tutorials n-dimensional input machine learning model higher-order tensors optional requires_grad option multi-dimensional output ticker import math evenly spaced values weights remain unchanged unpredictable learning results resulting column vector rigidly-structured model code cell throws advanced topic vector-valued function local derivatives needed single training batch neural network method works identically single-element output optional vector input vector-hessian product taking vector products direct access 2 single-element inputs important differential matrix vector-jacobian product detached copy lets local partial derivatives requires gradient tracking autograd shortcuts run multiple partial derivatives small slice print
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- How do we decide how far and in which direction to nudge the weights?
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- What Do We Need Autograd For?
- With all this machinery in place, how do we get derivatives out?
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