Building Deep Learning Models with TensorFlow
This course provides an introduction to deep learning using the TensorFlow library. Students will learn how to apply deep learning to unstructured data, such as images, sound, and text, to solve real-world problems. With the help of TensorFlow, students will gain the skills to uncover hidden structures in data and use them to their advantage. ▼
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Course Feature
Cost:
Free
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
10th Jul, 2023
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [February 21st, 2023]
What skills and knowledge will you acquire during this course?
By taking this course, learners will acquire a comprehensive understanding of the TensorFlow library and its capabilities. They will gain knowledge of foundational concepts such as the main functions, operations and the execution pipelines. Learners will also learn how to use TensorFlow for curve fitting, regression, classification and minimization of error functions. Additionally, they will explore different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Finally, learners will gain the skills to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. By the end of the course, learners will have the skills and knowledge to use TensorFlow to solve real world problems with deep learning.
How does this course contribute to professional growth?
This course provides learners with the skills and knowledge to use TensorFlow to solve real world problems with deep learning. It covers foundational concepts such as the main functions, operations and the execution pipelines, as well as more advanced topics such as curve fitting, regression, classification and minimization of error functions. Additionally, learners will gain an understanding of different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Through this course, learners will be able to develop a comprehensive understanding of the TensorFlow library and its capabilities, which will contribute to their professional growth.
Is this course suitable for preparing further education?
This course is suitable for preparing further education in the field of deep learning. It provides a comprehensive introduction to the TensorFlow library and its capabilities, as well as foundational concepts such as the main functions, operations and the execution pipelines. Learners will also gain an understanding of different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Additionally, learners will be able to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. By the end of the course, learners will have the skills and knowledge to use TensorFlow to solve real world problems with deep learning.
Pros & Cons
Good content and clear explanations.
Great lecturer.
Practical examples.
Outdated TensorFlow 1.
No audio/captions in some lectures.
No final assessment.
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