Building Deep Learning Models with TensorFlow(으)로 돌아가기
IBM의 Building Deep Learning Models with TensorFlow 학습자 리뷰 및 피드백
4.7
6개의 평가
강좌 소개
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.
최상위 리뷰
필터링 기준:
Building Deep Learning Models with TensorFlow의 2개 리뷰 중 1~2
교육 기관: Lam C V D
•Nov 06, 2019
course needed to be updated for labs. Now Google moved to Tensorflow 2.0 this year.
교육 기관: Martin K
•Nov 11, 2019
Good content. A bit too fast on some complex concepts and missing audio for the last lecture but great lecturer.
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