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Embedded Linux Penguin image Building Neural Networks with Linux

Course Overview
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.

Knowledge prerequisites
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.

Course Flow

1. Introduction
  • An in-depth look at Neural Networks and Python
  • An introduction to Neural Networks and neural network types
  • Convolutional Neural Networks
  • Python fundamentals
  • Set up of our development board: Raspberry Pi, Camera and Neural Compute Stick

2. Object Recognition

  • Implementation of object recognition with the Neural Compute Stick and Tensorflow

3. Sequential model and API, Compilation, trainig and layers with Keras

  • Multilayer Perceptron (MPL)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
4. Using pre-trained neural networks
  • Background
  • A practical look at using existing pre-trained neural networks
  • Implementation of a self-driving car platform

5. Neural Networks for Data Science

  • Background
  • Example: Value Estimations

6. Limitations, debugging and Topics of interest

  • We make time to look at the practical challanges of the course participants.

Administrative Information
Course Dates:
On Demand

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

More information
Phone: +32 (0)2 747 47 01
Course Registration Form

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