Deep learning is a sub-field of machine learning that has led to breakthroughs in a number of artificial intelligence tasks, achieving state-of-the-art performance in computer vision, speech recognition, and natural language processing. Not surprisingly, many companies are looking for ways to start applying deep learning to their business processes and data assets to realize the vision of an intelligent enterprise. However, building deep learning models and deploying them to enterprise applications requires specialized skills in neural networks, plus an understanding of enterprise engineering principals.
The objective of this course is to provide a hands-on introduction to deep learning, with emphasis on practical enterprise applications. Taking an engineering approach to deep learning, the course focuses on building deep neural network models for typical enterprise problems, including when to use deep learning, examples of industry applications, and how to deploy deep learning in enterprise systems. The course features experts from academia and industry to show different perspectives on deep learning. All examples are implemented using Google’s TensorFlow deep learning framework.
Week 1: Getting Started with Deep Learning Week 2: Shallow Neural Networks Week 3: Deep Networks and Sequence Models Week 4: Convolutional Networks Week 5: Industry Applications of Deep Learning Week 6: Advanced Deep Learning Topics Week 7: Final Exam
Markus Noga, Daniel Dahlmeier and Karthik Muthuswamy
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Dummie Variable completed this course.
I think this course wanted too much in too little time. As a beginner, a lot of expert terms are simply thrown at you and the explanations just confuse you. As a result, you catch a lot of keywords but don't really get a big picture. For an intermediate, the course never dives deep enough for you to understand the math and intuition behind everything. The course is stuck somewhere in the middle, too complicated for the beginner and too shallow for the ones who would like to understand the models.