This course is perfect for those wanting to make practical use of the world of possibilities opened by modern Machine Learning and Artificial Intelligence evolutions. The course starts by looking at the various Neural Network implementations and provides the minimal theoretical background needed to understand it. We look at implementation choices. We learn to use the most common implementations with Python and Keras. We use the Neural Compute stick with build in hardware accelerated Tensorflow network to implement in combination with a Raspberry Pi and a camera practical object recognition. We explore pre-build neural networks and learn how to build them. And apply our knowledge in a Data Science fashion too. Finally we look at limitations, debugging and topics of interest. During this course all participants will have the opportunity to build and experiment with a multifunctional small-footprint embedded target with powerfull hardware optimised neural network capabilities. After the course, the participants can take the board with them to continue experimenting.
IT Background and general Linux skills. Linux Power User or similar command line experience. Python knowledge is not required, but can be helpfull during the course. Some kind of programming experience is however advised to better understand the concepts. Method Course/Workshop, classical educations with practical exercises.
Participants Everybody who is responsible for designing and building Artificial Intelligence Systems.
2. Object Recognition
3. Sequential model and API, Compilation, trainig and layers with Keras
4. Using pre-trained neural networks
5. Neural Networks for Data Science
6. Limitations, debugging and Topics of interest
Courseware: Course materials provided, complemented with 1 book: Tutorial: Building Neural Networks with Linux, by Jasper Nuyens a free ARM-based Embedded Linux board with camera and Intel NCSM2450.DK1 Movidius Neural Compute Stick and a small self-driving car platform.
Price 3.150,- € + VAT
Phone: +32 (0)2 747 47 01
Course Registration Form
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