Deep Learning Training in Bratislava

This instructor-led live training in Bratislava (online or on-site) is designed for intermediate developers, data scientists, and AI practitioners aiming to utilize TensorFlow Lite for Edge AI applications.

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

• Comprehend the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Bratislava (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

Upon completing this training, participants will be able to:

• Construct and train convolutional neural networks (CNNs) utilizing TensorFlow.
• Utilize Google Colab for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques suitable for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Employ transfer learning to boost the performance of CNN models.
• Visualize and interpret the outcomes of image classification models.

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

By the conclusion 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.

This instructor-led, live training in Bratislava (online or onsite) is designed for advanced professionals who wish to explore 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.

This instructor-led, live training in Bratislava (online or onsite) is designed for intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts looking to deepen their expertise in text-to-image generation via deep learning.

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

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

This instructor-led, live training in Bratislava (online or onsite) is designed for advanced professionals aiming to utilize 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 Bratislava (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 fundamental principles of AlphaFold.
• Learn the operational mechanics of AlphaFold.
• Gain skills in interpreting AlphaFold predictions and results.

This instructor-led, live training in Bratislava (online or onsite) targets beginner to intermediate-level developers aiming to utilize Large Language Models for various natural language tasks.

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

• Configure a development environment featuring a popular LLM.
• Construct a basic LLM and fine-tune it using a custom dataset.
• Apply LLMs to various natural language tasks, such as text summarization, question answering, and text generation.
• Debug and evaluate LLMs using tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training (online or onsite) targets data scientists, machine learning engineers, and computer vision researchers aiming to utilize Stable Diffusion for generating high-quality images for various use cases.

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

• Understand the principles of Stable Diffusion and its mechanics 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 instructor-led, live training in Bratislava, participants will master the most relevant and cutting-edge machine learning techniques in Python by building a series of demo applications that utilize image, music, text, and financial data.

Upon completing this training, participants will be able to:

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

Applied AI from Scratch in Python empowers programmers and data analysts with the foundational techniques needed to build machine learning solutions from the ground up using Python. The course covers core principles of supervised learning, including classification and regression, as well as unsupervised learning techniques such as clustering and anomaly detection, alongside advanced neural network architectures. It examines proven methods for working with scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. This training helps professionals implement practical ML models, evaluate algorithm limitations, and complete applied projects designed for real-world problem solving.

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.

Delving into the fundamentals of artificial intelligence uncovers how intelligent systems are transforming digital strategy, automation, and decision-making processes within enterprise operations. The course covers core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, as well as communication, perception, and autonomous action. It equips executives and architects with the tools to evaluate AI-driven transformation opportunities, analyze emerging technology trends, and implement practical intelligent solutions to enhance business agility.

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.

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 instructor-led, live training in Bratislava (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 instructor-led, live training in Bratislava (online or onsite) is aimed at data scientists who wish 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.

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

This instructor-led live training, delivered Bratislava (online or onsite), targets developers and data scientists aiming to utilize TensorFlow 2.x for building predictors, classifiers, generative models, neural networks, and more.

By the conclusion 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 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