Deep Learning Training in Belgrade

This instructor-led, live course in Belgrade (online or onsite) targets intermediate developers, data scientists, and AI professionals aiming to utilize TensorFlow Lite for Edge AI initiatives.

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

• Comprehend the foundational aspects of TensorFlow Lite and its role in Edge AI.
• Build and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on diverse edge devices.
• Employ tools and techniques for model conversion and optimization.
• Develop practical Edge AI applications using TensorFlow Lite.

This instructor-led live training Belgrade (available online or on-site) targets advanced professionals who aim to deepen their grasp of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

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

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Belgrade (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

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

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This instructor-led, live training in Belgrade (online or onsite) targets advanced-level professionals seeking to specialize in cutting-edge deep learning techniques for NLU.

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

• Understand the key differences between NLU and NLP models.
• Apply advanced deep learning techniques to NLU tasks.
• Explore deep architectures such as transformers and attention mechanisms.
• Leverage future trends in NLU for building sophisticated AI systems.

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 instructor-led, live training in Belgrade (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts seeking to expand their knowledge and skills in deep learning for text-to-image generation.

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

• Grasp advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Enhance performance and scalability for large datasets and complex models.
• Tune hyperparameters to improve model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in Belgrade (online or onsite) is tailored for advanced professionals who aim to leverage AI techniques to revolutionize drug discovery and development processes.

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

• Understand the role of AI in drug discovery and development.
• Apply machine learning techniques to predict molecular properties and interactions.
• Use deep learning models for virtual screening and lead optimization.
• Integrate AI-driven approaches into the clinical trial process.

This instructor-led, live training in Belgrade (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This instructor-led, live training in Belgrade (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

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

• Set up a development environment that includes a popular LLM.
• Create a basic LLM and fine-tune it on a custom dataset.
• Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
• Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

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

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

In this live, instructor-led training in Belgrade, participants will explore the most current and advanced machine learning techniques in Python by developing a series of demonstration applications that utilize image, audio, text, and financial data.

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

• Implement machine learning algorithms and techniques to address complex problems.
• Apply deep learning and semi-supervised learning methods to applications involving images, music, text, and financial data.
• Maximize the potential of Python algorithms.
• Utilize libraries and packages such as NumPy and Theano.

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.

An exploration of the foundational principles of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation, and decision-making processes within enterprise operations. This course examines core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside topics such as communication, perception, and autonomous action. It equips executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technology trends, and implement practical intelligent solutions to enhance 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 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.

This guided, live training in Belgrade (online or onsite) is designed for software developers, data analysts, and technical professionals who wish to use TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.

This instructor-led live training in Belgrade (online or on-site) is designed for data scientists who want to use TensorFlow to analyze potential fraud data.

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

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

Through this instructor-led live training, participants will learn how to utilize 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

This instructor-led, live training Belgrade (online or onsite) is designed for developers and data scientists interested in utilizing TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and other solutions.

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

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

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