Retour à Building Deep Learning Models with TensorFlow
Avis et commentaires pour l'étudiant pour Building Deep Learning Models with TensorFlow par IBM
4.1
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À propos du cours
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.
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1 - 6 sur 6 Examens pour Building Deep Learning Models with TensorFlow
par Lam C V D
•Nov 06, 2019
course needed to be updated for labs. Now Google moved to Tensorflow 2.0 this year.
par Shinhoo K
•Nov 17, 2019
The codes need to be updated for TensorFlow 2.0.
par Martin K
•Nov 22, 2019
Good content. A bit too fast on some complex concepts and missing audio for the last lecture but great lecturer.
par Tony H
•Nov 18, 2019
Mostly trivial quiz questions and no graded practical work. The certificate is therefore not worth very much.
par John R H A
•Dec 07, 2019
Teaches more on Deep Learning models but less in TensorFlow
par dk
•Nov 26, 2019
这什么课？即没多少Deep Learning
的内容，也没多少TENSORFLOW的内容？
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