Introduction to the Use of Neural Networks Training Course

This course explores the application of AI—specifically Machine Learning and Deep Learning—within the automotive sector. It aims to identify technologies that can be (potentially) utilized across various automotive scenarios: from basic automation and image recognition to autonomous decision-making.

Designed for managers, solutions architects, innovation specialists, CTOs, software architects, and anyone eager to understand applied artificial intelligence and its near-term evolutionary trajectory.

This instructor-led, live training in Greece (online or onsite) is designed for 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 creation 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 constitutes a specialized subset of Machine Learning.

This is a four-day course introducing AI and its applications. There is an option to have an additional day to undertake an AI project upon completion of this course.

This comprehensive four-day course provides an introduction to Artificial Intelligence and its practical implementation using the Python programming language. Upon successful completion, participants have the option to extend their learning with an additional day dedicated to executing a real-world AI project.

This instructor-led, live training in Greece (online or onsite) is designed for 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 creation of Artificial Intelligence (AI) systems that can execute "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a subset of Machine Learning.

This instructor-led live training in Greece (online or onsite) is designed for researchers and developers who wish to use Chainer to build and train neural networks in Python, 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 begin 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 Greece (online or onsite) provides an introduction to the fields of pattern recognition and machine learning. It covers 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 numerous modern AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to acquire strategies, refine policies, and execute autonomous decisions through trial-and-error processes guided by reward mechanisms.

This instructor-led live training (available online or onsite) targets intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. Participants will learn to construct intelligent agents capable of making autonomous decisions within complex environments.

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

• Comprehend the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
• Implement core RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents leveraging TensorFlow or PyTorch.
• Deploy DRL solutions in practical applications, including gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and enhance training performance using contemporary tools.

Format of the Course

• Interactive lectures accompanied by guided discussions.
• Practical exercises and real-world implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (for instance, substituting TensorFlow with PyTorch), please contact us to arrange.

This instructor-led training programme combines presentations with hands-on, 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 software environment.

This course provides a conceptual foundation in neural networks and broadly covers machine learning algorithms, deep learning (algorithms and applications).

Part-1 (40%) of this training focuses heavily on fundamentals, helping you select 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 extensively based on TensorFlow, Google's open-source software library API for Deep Learning. All examples and hands-on exercises will be conducted using 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
• comprehend TensorFlow’s structure and deployment mechanisms
• be capable of performing installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-level tasks such as training models, building graphs, and logging

This instructor-led, live training in Greece (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.