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Updated in [June 30th, 2023]

Practical Deep Learning For Coders is a 7-week course designed for those with at least one year of coding experience and some knowledge of high-school math. The course covers topics from getting a GPU server online suitable for deep learning to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. It consists of around 20 hours of lessons, and students should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF. Part 2 will be taught at the Data Institute from Feb 27, 2017, and will be available online around May 2017.

[Applications]
The application of this course can be seen in various areas such as computer vision, natural language processing, and recommendation systems. After completing this course, students can apply their knowledge to create state-of-the-art models for these areas. Additionally, students can use the skills they have learned to develop their own projects and applications. Furthermore, students can use the knowledge they have gained to pursue further studies in deep learning.

[Career Path]
One job position path recommended for learners of this course is a Deep Learning Engineer. Deep Learning Engineers are responsible for developing and deploying deep learning models to solve complex problems. They must have a strong understanding of machine learning algorithms, deep learning frameworks, and software engineering principles. They must also be able to work with large datasets and have experience with distributed computing.

The development trend of Deep Learning Engineers is to become more specialized in their field. As deep learning technology advances, engineers must stay up to date with the latest developments and be able to apply them to their work. They must also be able to work with different types of data, such as images, text, and audio. Additionally, they must be able to work with different types of hardware, such as GPUs and CPUs. As deep learning technology continues to evolve, Deep Learning Engineers must be able to adapt and stay ahead of the curve.

[Education Path]
The recommended educational path for learners of this course is a Bachelor's degree in Computer Science with a focus on Software Security. This degree will provide students with a comprehensive understanding of the fundamentals of software security, including topics such as software vulnerabilities, attacks, and defenses. Students will also gain an understanding of the development cycle and how to use techniques at each phase to strengthen the security of software systems. Additionally, students will learn how to use advanced testing and program analysis techniques to identify and mitigate software vulnerabilities.

The development trend for this degree is to focus on the latest technologies and techniques in software security. This includes topics such as cloud security, mobile security, and artificial intelligence security. Additionally, students will learn about the ethical implications of software security and the importance of protecting user data. As technology continues to evolve, the degree will also focus on emerging technologies and their implications for software security.