Neural Networks Training in Prague

Delivered as an instructor-led, live training in Prague (online or onsite), this program is designed for advanced professionals interested in exploring state-of-the-art XAI techniques for deep learning models, focusing on the development of 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.

The course 'Applied AI from Scratch in Python' provides programmers and data analysts with the essential techniques required to develop machine learning solutions entirely from the ground up using Python. The curriculum covers the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. Participants will explore proven methodologies for leveraging tools like scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program is designed to help professionals implement practical ML models, critically evaluate the limitations of various algorithms, and complete applied projects aimed at solving real-world problems.

Deep Reinforcement Learning (DRL) integrates reinforcement learning principles with deep learning architectures to empower agents to make decisions through environmental interaction. This technology drives numerous modern 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 decisions via trial-and-error reward-based learning.

This instructor-led live training (available online or onsite) targets intermediate-level developers and data scientists who want to master and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in 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 optimize training performance with modern tools.

Course Format

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

Course Customization Options

• To request a customized version of this course (e.g., focusing on PyTorch instead of TensorFlow), please contact us to make arrangements.

A deep dive into the foundations of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making capabilities throughout enterprise operations. The curriculum covers essential topics such as the evolution of AI, problem-solving frameworks, methods for representing knowledge, handling uncertainty in reasoning, and various machine learning approaches. It also addresses communication, perception, and autonomous systems. This course empowers executives and system architects to assess opportunities for AI-driven transformation, stay informed about emerging technological trends, and implement practical intelligent solutions to enhance business agility.

This course provides a comprehensive overview of AI, with a focus on Machine Learning and Deep Learning, within the automotive industry. It explores which technologies can be effectively applied across various vehicle scenarios, ranging from basic automation and image recognition to autonomous decision-making processes.

An Artificial Neural Network is a computational data model utilized in the development of Artificial Intelligence (AI) systems capable of executing "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 Machine Learning.

This instructor-led live training in Prague (available online or on-site) 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 Prague (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 course provides conceptual knowledge of neural networks and, more broadly, of machine learning and deep learning algorithms and applications.

Part 1 (40%) of the training focuses 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 that simplifies the creation of deep learning models.

Part 3 (40%) of the training is extensively based on TensorFlow, the API for 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 looking to utilize TensorFlow for their Deep Learning projects.

Upon completion of 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 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