Introduction to the Use of Neural Networks Training Course

This course explores the application of AI—specifically focusing on Machine Learning and Deep Learning—within the automotive industry. It enables participants to identify which technologies can be (potentially) applied across various vehicle scenarios, ranging from basic automation and image recognition to autonomous decision-making.

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

This instructor-led live training in Slovakia (online or onsite) is targeted at beginner-level participants who want to master essential concepts in probability, statistics, programming, and machine learning to 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 utilized in the development of Artificial Intelligence (AI) systems designed to perform "intelligent" tasks. Neural Networks are widely applied in Machine Learning (ML) applications, which represent one specific implementation of AI. Deep Learning is a specialized subset of Machine Learning.

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

This four-day course provides an introduction to AI and its applications using the Python programming language. Upon completion, participants have the option to extend their learning by dedicating an additional day to working on a practical AI project.

This instructor-led, live training in Slovakia (online or onsite) targets intermediate-level data scientists and statisticians aiming to prepare data, build models, and apply machine learning techniques effectively in their professional fields.

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 Networks are computational data models employed in the creation of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a specialized subset of Machine Learning.

This instructor-led live training in Slovakia (online or onsite) is designed for 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 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 Slovakia (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 the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions by interacting with their surroundings. This approach drives many contemporary AI breakthroughs, including self-driving cars, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make autonomous choices through trial-and-error processes guided by reward signals.

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 to create intelligent agents capable of autonomous decision-making within complex environments.

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

• Grasp the theoretical foundations and mathematical principles underlying Reinforcement Learning.
• Implement core 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.
• Troubleshoot, visualize, and enhance training performance using modern toolsets.

Format of the Course

• Interactive lectures paired with 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 instance, using PyTorch instead of TensorFlow), please contact us to arrange.

In this instructor-led live training, participants will acquire the skills to design, construct, and visualize convolutional neural networks for image recognition using Matlab.

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

• Construct deep learning models
• Automate the data labeling process
• Interoperate with models from Caffe and TensorFlow-Keras
• Train data utilizing multiple GPUs, cloud environments, or clusters

Audience

• Developers
• Engineers
• Domain experts

Course Format

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

This instructor-led training session combines presentations with hands-on computer-based examples and case study exercises utilizing relevant neural and deep learning libraries.

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

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

Part 1 (40%) of this training focuses heavily on fundamentals, which will assist you in selecting the appropriate technology stack: 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%) is extensively dedicated to 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 aim to leverage TensorFlow for their Deep Learning projects.

Upon completing this course, participants will:

• possess a comprehensive understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend the structure and deployment mechanisms of TensorFlow
• be capable of performing 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-like tasks such as training models, building graphs, and logging