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Opiniones y comentarios de aprendices correspondientes a Building Deep Learning Models with TensorFlow por parte de Habilidades en redes de IBM

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The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems. Learning Outcomes: After completing this course, learners will be able to: • explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines. • describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. • understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. • apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained....

Principales reseñas

ZR

2 de jul. de 2020

Deep Learning made me feel that there is a way to build models and classify data so easily and in a skillful way. Amazing course!

DO

26 de may. de 2020

Not so often i wish a course would be longer and more in depth I really enjoyed using TF I'll look some other courses about it

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