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Neural Networks Fundamentals using TensorFlow course contents
The Neural Networks Fundamentals TensorFlow course in Boston will initialize together with TensorFlow introduction for example creation, restoring the variable, preloading, reading, foundation of TensorFlow and the way how to imagine and assess models, TensorFlow mechanics, re-establishing the factors, for example assembling the charts, placeholders, inputs, from observation to Support vector machines, paired and record grouping and confinements of the recognition, preparing and assessing the models, learning calculations, Feedforward and feedback Neural Networks, nonlinear decision limits, prologue to Artificial Neural Networks, Convolutional Neural Networks and their objectives and model engineering in addition to standards, as well as code associations and propelling in addition to preparing the model, limiting the cost work, forward and back spread, also multilayer discernments, research study plus a project.
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