Explainability in Deep Learning: Demystifying Black-Box Models Training Course

This instructor-led, live training in Belgium (online or onsite) is aimed at advanced-level professionals who wish to enhance their understanding of XAI techniques for complex AI models.

By the end of this training, participants will be able to:

• Implement state-of-the-art XAI techniques in AI models.
• Interpret deep learning models and their decisions.
• Apply advanced model-agnostic and model-specific explainability methods.
• Address challenges related to AI transparency in complex systems.

This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.

This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.

This instructor-led, live training in Belgium (online or onsite) is aimed at beginner-level participants who wish to learn essential concepts in probability, statistics, programming, and machine learning, and apply these to AI development.

By the end of this training, participants will be able to:

• Understand basic concepts in probability and statistics, and apply them to real-world scenarios.
• Write and understand procedural, functional, and object-oriented programming code.
• Implement machine learning techniques such as classification, clustering, and neural networks.
• Develop AI solutions using rules engines and expert systems for problem-solving.

Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.

This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course. 

This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. 

This instructor-led, live training in Belgium (online or onsite) is aimed at intermediate-level data scientists and statisticians who wish to prepare data, build models, and apply machine learning techniques effectively in their professional domains.

By the end of this training, participants will be able to:

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post hoc analyses and visualize results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.

This instructor-led, live training in Belgium (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using a comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This instructor-led, live training in Belgium (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This instructor-led, live training in Belgium (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

By the end of this training, participants will be able to:

• Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
• Apply advanced Reinforcement Learning algorithms to solve real-world problems.
• Build a Deep Learning Agent.

This instructor-led, live training in Belgium (online or onsite) is aimed at beginner-level professionals who wish to learn the basics of Explainable AI and its role in developing responsible AI systems.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of Explainable AI.
• Explore the importance of transparency and interpretability in AI models.
• Learn basic techniques for making AI models explainable.
• Apply XAI techniques to simple machine learning models.

This instructor-led, live training in Belgium (online or onsite) is aimed at intermediate-level professionals who wish to apply Explainable AI techniques to ensure fairness, transparency, and ethical AI systems.

By the end of this training, participants will be able to:

• Understand the role of XAI in ethical AI systems.
• Implement XAI techniques to detect and mitigate bias in AI models.
• Ensure transparency in AI model decision-making processes.
• Align AI development with ethical and regulatory standards.

This instructor-led, live training in Belgium (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to implement transparent AI systems in real-world applications.

By the end of this training, participants will be able to:

• Understand the importance of transparency in AI models.
• Implement advanced XAI techniques to interpret complex models.
• Enhance model transparency using SHAP, LIME, and other tools.
• Address ethical concerns and fairness in AI models.