Specialist In Automotive Software And AI Development Solutions
🚀 Hands-On Boot Camp: Implementing Continuous Integration in Embedded Systems Development! 🚀
🚀 Hands-On Boot Camp: Implementing Continuous Integration in Embedded Systems Development! 🚀
🚀 Hands-On Boot Camp: Implementing Continuous Integration in Embedded Systems Development! 🚀
Gain the skills and knowledge needed to integrate the enormous potential of artificial intelligence (AI) into your product portfolio and value chain.
From autonomous driving to material testing and medical image processes, artificial intelligence (AI) and machine learning (ML) are being integrated into an increasing number of technologies in a broad range of industries and applications. To meet evolving industry and market demands, companies must be able to understand the basic elements of AI and ML.
We cover the foundations of AI and ML in our UL Certified Artificial Intelligence Professional training. In this hands-on learning experience, we cover the skills and knowledge participants need to integrate the enormous potential of AI into their product portfolio and value chain. This practice-oriented training covers ML methods, with a particular focus on artificial neural networks, the basis for deep learning, and features applied AI and ML exercises.
Upon successful completion of this workshop, participants will be able to:
Describe terms and concepts commonly used when creating and maintaining artificial intelligence systems.
Understand underlying techniques, use cases and drawbacks of different artificial intelligence methods.
Design, train and validate neural networks from scratch using modern frameworks.
Take the UL Certified Artificial Intelligence Professional — Foundations exam, a prerequisite for advanced certifications, such as UL Certified Autonomy Safety Professional — Machine Learning.
Day 1: Basics
Introduction and definition of terms
Use cases and shortcomings of AI
Presentation of technologies used in the workshop, such as TensorFlow and Keras
Example architectures
Landscape of AI methods
Supervised learning
Unsupervised learning
Reinforcement learning
Single-layer neuronal networks (perceptrons)
Biological motivation
Biological and artificial neurons
Learning: optimization and gradient descent
Classification of multiple classes
Day 2: Overview of terms and tools
Loss functions
Performance metrics
Data partitioning
Feature extraction and dimensionality reduction
Overfitting and countermeasures
Multi-layer neural networks
Backpropagation
Deep learning
Introduction to convolution
Convolutional neural networks
Convolutional neural networks for image-based problems
Target Audience
Software engineers, developers, data scientists, project leaders, quality managers and testing personnel who are developing artificial intelligence systems.
Engineers working with advanced driver assistance systems (ADAS) and autonomous vehicle (AV) verification.