Online Neural Networks Training in Serbia

Delivered as an instructor-led, live training in Serbia (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.

Practical AI Implementation from Scratch in Python provides developers and data analysts with the essential skills needed to construct machine learning models from the ground up using Python. The course covers the fundamental concepts of supervised learning, including classification and regression, as well as unsupervised learning techniques such as clustering and anomaly detection, alongside advanced neural network structures. Participants will explore established methods for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. This training enables professionals to deploy effective ML models, assess algorithm constraints, and execute applied projects designed to address real-world challenges.

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.

Exploring artificial intelligence fundamentals reveals how intelligent technology reshapes digital strategy, automation, and decision making across enterprise operations. Examines core concepts spanning AI history, problem-solving frameworks, knowledge representation, uncertain reasoning, and machine learning paradigms alongside communication, perception, and autonomous action. Guides executives and architects to evaluate AI-driven transformation opportunities, assess emerging technology trends, and integrate practical intelligent solutions to accelerate business agility.

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.

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 instructor-led, live training in Serbia (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 Serbia (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.

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