Neural Networks Training in Oslo

This instructor-led, live training in Oslo (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.

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

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.

This is a four-day course that provides an introduction to artificial intelligence (AI) and its applications using the Python programming language. Participants also have the option to extend their learning with an extra day dedicated to working on an AI project after completing the course.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning architectures, enabling agents to make decisions by interacting with their environments. This technology is behind many contemporary AI advancements, such as self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL allows artificial agents to learn strategies, optimize policies, and make autonomous decisions through a reward-based trial-and-error process.

This instructor-led, live training (available online or on-site) is designed for intermediate-level developers and data scientists who want to learn and apply Deep Reinforcement Learning techniques to create intelligent agents capable of making autonomous decisions in complex environments.

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

• Understand the theoretical foundations and mathematical principles underlying Reinforcement Learning.
• Implement key RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Build and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to real-world scenarios like games, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance using modern tools.

Format of the Course

• Interactive lecture and guided discussion.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (for example, using PyTorch instead of TensorFlow), please contact us to arrange.

This course has been designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone else who is keen to gain an overview of applied artificial intelligence and its near-term development forecast.

This course explores AI, with a focus on Machine Learning and Deep Learning, within the automotive industry. It provides insights into how these technologies can be potentially utilized in various scenarios within a vehicle, ranging from basic automation and image recognition to advanced autonomous decision-making.

This instructor-led, live training in Oslo (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.

An Artificial Neural Network is a computational data model utilized in the development of Artificial Intelligence (AI) systems that can perform "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning is a specialized subset of Machine Learning.

This is a four-day course that introduces artificial intelligence (AI) and its applications. Participants have the option to extend their learning by adding an extra day to work on an AI project upon completing the course.

This instructor-led, live training in Oslo (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.

An Artificial Neural Network is a computational data model utilized in the development of Artificial Intelligence (AI) systems that can perform "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning is a specialized subset of Machine Learning.

This instructor-led, live training in Oslo (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 Oslo (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.

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

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

• Create a deep learning model
• Automate data labeling processes
• Work with models from Caffe and TensorFlow-Keras
• Train data using multiple GPUs, cloud resources, or clusters

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• A combination of lectures, discussions, exercises, and extensive hands-on practice

This training is designed for individuals who wish to gain an understanding of the fundamentals of neural networks and their practical applications.

This classroom-based training session will include presentations and computer-based examples, along with case study exercises that utilize relevant neural and deep network libraries.

This course provides an introduction to implementing neural networks for practical applications using the R-project software.

This course starts by providing you with a solid foundation in neural networks and machine learning algorithms, including deep learning concepts and applications.

The first part (40%) of this training focuses on the fundamentals, helping you choose the right technology from options like TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

The second part (20%) introduces Theano, a Python library that simplifies the process of writing deep learning models.

The third part (40%) of the training is extensively based on TensorFlow, Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers who want to use TensorFlow for their deep learning projects.

After completing this course, delegates will:

• have a thorough understanding of deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN)
• understand TensorFlow’s architecture and deployment mechanisms
• be capable of handling installation, production environment setup, architectural tasks, and configuration
• be able to evaluate code quality, perform debugging, and monitor performance
• be proficient in implementing advanced production tasks such as training models, building computational graphs, and logging