AI Education


AI Education

In the era of the 4th industrial revolution, it is necessary to educate talented people who can apply artificial intelligence deep learning technology to various industrial fields. Deep Runner Z1E is a core equipment necessary for the practice of artificial intelligence application technology education. Guarantees maximum training effectiveness at minimum cost.

< Deep Runner Z1E >
< Deep Runner Z1E >

Applications

  • Artificial intelligence deep learning practice education at universities, academies, and workplaces

Features

  • Provide deep learning core functions : It provides the classification function and object detection function, which are the core functions of image-based deep learning technology.


  • Educating the entire deep learning process : It provides a comprehensive environment where students can practice the entire process necessary for normal deep learning applications in a short lecture time. Students can easily experience the process of building training data, model learning, and real-time image recognition without complex coding. In addition, students can focus on training in a short time to build intelligent systems by combining artificial intelligence devices with IT systems. Each level can be completed in 1-2 hours. In parallel with the theory class, students' learning satisfaction can be increased.


  • Variety of curriculum design : The deep learning model learning target can be configured with various modifications such as factory quality inspection, security system, smart city, and smart signage system. In addition, students can practice implementing intelligent systems in various fields depending on the industry they are aiming for. We provide a sample tutorial for this.


  • Provides an objective academic evaluation method : The deep learning model training results performed by each student can be clearly evaluated as a recognition rate using the instructor's evaluation dataset. This allows students to focus their education on data collection techniques to increase recognition accuracy.


  • Minimum configuration : In addition to the student's computer, one small training device Deep Runner is sufficient. Since a laptop screen and built-in camera are used, there is no need for a separate monitor and camera, so theory lectures and practical exercises can be performed on the same desk.


  • Remote sharing of practice results with instructors : Once the IP address and password are known, the instructor can easily access the results of each student's practice for instruction and evaluation.

Based chipset

  • Xilinx Zynq 7020 SoC

Deep Learning Lectures and Practice Items