課程概述

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....

學生對 IBM 提供的 Building Deep Learning Models with TensorFlow 的評價和反饋

課程概述

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26 - Building Deep Learning Models with TensorFlow 的 50 個評論（共 107 個）

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學生對 IBM 提供的 Building Deep Learning Models with TensorFlow 的評價和反饋

課程概述

熱門審閱

26 - Building Deep Learning Models with TensorFlow 的 50 個評論（共 107 個）

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熱門在線課程

熱門在線專項課程

在線證書

在線學位課程

Coursera

社區

更多