Given the excellent performance of deep learning methods in the past decade, machine learning researchers today are slowly transitioning from building task-specific neural networks to designing complex systems that can perform machine learning on multiple problems and datasets. These systems are called AutoML Systems and is one of the thriving areas of research in deep learning. In this class, we will begin by implementing a simple image recognition task and then extend it for a more complex scenario using some state of the art techniques in AutoML. Finally, we will wrap up with a quick overview of some of the key techniques and concepts in AutoML and transfer learning. By the end of the class, you would have two coded two projects to get you started in the deep learning architecture design. The ones who shall benefit the most from this class are the ones who have some experience with deep learning methods, however, anyone with a basic understanding of the key terminologies in machine learning and a good grasp of Python shall be able to follow along.

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