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Updated in [June 30th, 2023]

This course, Deep Learning Explained, provides an intuitive approach to building complex models that help machines solve real-world problems with human-like intelligence. Learners will gain an understanding of the key concepts behind this game changing technology, and learn simple yet powerful "motifs" that can be used with lego-like flexibility to build an end-to-end deep learning model. The course will use the Microsoft Cognitive Toolkit -- previously known as CNTK -- to harness the intelligence within massive datasets through deep learning with uncompromised scaling, speed, and accuracy. Learners will use Python Jupyter notebooks running on their local Windows or Linux machine, or on a virtual machine running on Azure. Alternatively, they can leverage the Microsoft Azure Notebooks platform for free. Financial assistance is available for those who may not be able to pay the fee. This course will retire in June, so please enroll only if you are able to finish your coursework in time.

[Applications]
Upon completion of this course, learners will have the knowledge and skills to apply deep learning to solve real-world problems. They will be able to use the Microsoft Cognitive Toolkit to build and derive insights from deep learning models. Learners will also be able to leverage the Microsoft Azure Notebooks platform for free to develop and deploy deep learning models. Additionally, learners will be able to apply for financial assistance to earn Verified Certificates.

[Career Path]
One job position path recommended for learners of this course is a Deep Learning Engineer. A Deep Learning Engineer is responsible for developing and deploying deep learning models to solve complex problems. They must have a strong understanding of machine learning algorithms and techniques, as well as the ability to design and implement deep learning models. They must also be able to evaluate the performance of the models and optimize them for better results. Additionally, they must be able to work with large datasets and have experience with programming languages such as Python, R, and C++.

The development trend for Deep Learning Engineers is to focus on developing more efficient and accurate models, as well as to explore new applications of deep learning. Additionally, they must stay up to date with the latest advancements in deep learning technology and be able to apply them to their work. As deep learning becomes more widely used, Deep Learning Engineers will be in high demand, and the job market for this position is expected to continue to grow.

[Education Path]
The recommended educational path for learners is to pursue a Master's degree in Computer Science with a focus on Artificial Intelligence for Robotics. This degree will provide students with the knowledge and skills necessary to develop and program robotic systems. Students will learn the fundamentals of Artificial Intelligence, including probabilistic inference, planning and search, localization, tracking and control, and how to apply these methods in the context of building self-driving cars. Additionally, students will gain experience in programming and working with robotic systems.

The development trend for this degree is to focus on the application of Artificial Intelligence in robotics. This includes the development of autonomous vehicles, robots for medical and industrial applications, and robots for home use. Additionally, the development of Artificial Intelligence algorithms and techniques for robotics is becoming increasingly important. As technology advances, the need for more sophisticated Artificial Intelligence algorithms and techniques for robotics will continue to grow.