Neural Networks Training in Belgrade

Delivered as an instructor-led, live training in Belgrade (online or onsite), this course is designed for advanced professionals who wish to explore cutting-edge 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.

This four-day course provides an introduction to Artificial Intelligence and its practical applications using the Python programming language. Upon completing the course, there is an option to dedicate an additional day to working on a hands-on AI project.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, allowing agents to make decisions through interaction with their environments. This technology supports many modern AI advancements, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make autonomous decisions based on 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 learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.

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

• Grasp the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement key RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Build and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to real-world applications such as 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 (e.g., using PyTorch instead of TensorFlow), please contact us to arrange.

Designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone curious about applied artificial intelligence and its near-term trajectory, this course provides a comprehensive overview.

This course provides an overview of AI, with a focus on Machine Learning and Deep Learning, within the Automotive sector. It enables participants to identify which technologies can be (potentially) applied in various car scenarios, ranging from basic automation and image recognition to autonomous decision-making.

This instructor-led, live training in Belgrade (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.

Artificial Neural Networks (ANNs) are computational models designed for developing Artificial Intelligence (AI) systems that can perform complex, "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which represent one of the primary implementations of AI. Deep Learning constitutes a specialized subset within the broader field of Machine Learning.

This four-day course provides an introduction to Artificial Intelligence and its practical applications. Upon completing the course, participants have the option to dedicate an additional day to working on a dedicated AI project.

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

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 creating Artificial Intelligence (AI) systems that can execute "intelligent" tasks. These networks are frequently employed within Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a specialized subset of ML.

This instructor-led, live training in Belgrade (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 Belgrade (online or onsite) offers 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.

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:

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

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

The course is designed for individuals interested in learning the fundamentals of neural networks and their practical applications.

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

This course provides an introduction to utilizing neural networks for solving real-world challenges with the R programming environment.

This program initiates with conceptual knowledge in neural networks and generally in machine learning algorithms, deep learning (algorithms and applications).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3(40%) of the training would be extensively based on Tensorflow - API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• have a good understanding on deep neural networks(DNN), CNN and RNN
• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, building graphs and logging