In this course you will learn the fundamentals of Deep Learning primarily through a series of hands on exercises guided by the instructor. Students will learn about the foundational underpinnings of machine learning and deep learning as well as how to put that knowledge to the test with practical exercises.
The course takes unique project focused approach to teach you deep learning by building deep learning models. The instructor will walk you through a series of curated projects, and explain the key concepts as they arise. Students will learn the theory and how these models work under the hood while writing code, and building neural networks.
The course will balance learning theory and working on projects
Students who take this course will be able to:

Identify and frame problems that can be solved by deep learning

Choose the right techniques to the problems

Understand key deep learning concepts and how deep learning models work

Identify and fix problems with messy datasets

Build deep neural nets for classification and regression using the Keras framework

Build convolutional neural networks for image classification, object localization and segmentation using the Keras

Discuss the parts and processes involved in building large scale deep learning applications

Full refund upon request before the first session ends (Jan 4th, 2021 12:00pm PST). 5% transaction fee is not refundable.

If miss the live sessions, you can watch recorded sessions any time, along with interactive learning tools, slides, course notes