Introduction to the Use of Neural Networks Training Course

This course explores the application of AI, with a focus on Machine Learning and Deep Learning, within the automotive sector. It guides participants in identifying suitable technologies for various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making systems.

This course is designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone seeking a comprehensive overview of applied artificial intelligence and its near-term development trajectory.

This instructor-led live training in Norway (online or onsite) is designed for beginner-level participants who want to learn essential concepts in probability, statistics, programming, and machine learning, and apply them 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 employed in creating Artificial Intelligence (AI) systems that can execute "intelligent" tasks. Neural Networks are widely utilized in Machine Learning (ML) applications, which serve as a specific implementation of AI. Deep Learning represents a specialized subset of Machine Learning.

Spanning four days, this course provides a foundational introduction to artificial intelligence and its practical applications. Participants may also choose to extend their learning by dedicating an additional day to working on a real-world AI project upon completing the course.

This is a 4-day course that introduces AI and its application using the Python programming language. Participants can opt for an extra day to undertake an AI project upon completion of this course.

This instructor-led, live training in Norway (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 building Artificial Intelligence (AI) systems that can carry out "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a specific subset of ML.

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

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions by interacting with their environments. This technology drives many contemporary AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, optimize policies, and execute autonomous decisions through trial-and-error mechanisms based on reward signals.

This instructor-led live training is available both online and onsite. It is designed for intermediate-level developers and data scientists who want to master and apply Deep Reinforcement Learning techniques to create intelligent agents capable of making autonomous decisions in complex settings.

Upon completion of this training, participants will be able to:

• Grasp the theoretical foundations and mathematical concepts underlying Reinforcement Learning.
• Implement essential RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical applications like gaming, robotics, and decision optimization.
• Use modern tools to troubleshoot, visualize, and optimize training performance.

Format of the Course

• Interactive lectures accompanied by guided discussions.
• Practical exercises and hands-on 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 make arrangements.

This in-person training program comprises presentations along with computer-based examples and case study exercises utilizing relevant neural and deep network libraries.

This course provides an introduction to implementing neural networks for real-world problems using the R programming environment.

This course initiates with a conceptual foundation in neural networks, covering broad aspects of machine learning algorithms and deep learning (including algorithms and their applications).

Part-1 (40%) of this training concentrates on fundamentals, aiding you in selecting the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part-2 (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part-3 (40%) of the training is heavily focused on TensorFlow, the API for Google's open-source deep learning software library. All examples and hands-on exercises will utilize TensorFlow.

Audience

This course is designed for engineers aiming to utilize TensorFlow for their deep learning projects.

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow's structure and deployment mechanisms
• be capable of handling installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitor systems
• be able to implement advanced production tasks such as training models, building graphs, and logging

This instructor-led, live training in Norway (online or onsite) is designed for advanced professionals eager to investigate cutting-edge XAI techniques for deep learning models, with a strong emphasis on developing interpretable AI systems.

Upon completion of this training, participants will be able to:

• Grasp the challenges associated with explainability in deep learning.
• Deploy advanced XAI techniques for neural networks.
• Analyze decisions generated by deep learning models.
• Assess the trade-offs between performance and transparency.