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Updated in [February 21st, 2023]

What skills and knowledge will you acquire during this course?
This course will provide learners with the skills and knowledge necessary to build deep learning models using the Keras library. Learners will gain an understanding of unsupervised and supervised deep learning models, and be able to apply these models to real-world problems. Additionally, learners will gain an understanding of the fundamentals of deep learning and neural networks, and be able to apply these concepts to their own projects. Finally, learners will gain an understanding of the latest developments in deep learning and neural networks, and be able to stay up to date on the latest research.

How does this course contribute to professional growth?
This course provides a comprehensive introduction to the field of deep learning and neural networks, and equips learners with the skills to build deep learning models using the Keras library. By completing this course, learners will gain an understanding of unsupervised and supervised deep learning models, and be able to apply their knowledge to pursue further education or a career in deep learning. Additionally, learners can supplement this course with additional courses in related topics, such as machine learning, natural language processing, and computer vision, and practice building deep learning models on their own, using open source datasets and libraries. This course thus contributes to professional growth by providing learners with the necessary skills and knowledge to pursue further education or a career in deep learning.

Is this course suitable for preparing further education?
This course provides an introduction to the field of deep learning and neural networks, and teaches learners how to build deep learning models using the Keras library. It is suitable for preparing further education in deep learning and neural networks, such as a master's degree in artificial intelligence or a PhD in computer science. Learners should supplement this course with additional courses in related topics, such as machine learning, natural language processing, and computer vision, and practice building deep learning models on their own, using open source datasets and libraries. Additionally, they should stay up to date on the latest developments in deep learning and neural networks.