Conducted either online or on-site, these instructor-led live Deep Learning (DL) training courses provide a practical, hands-on approach to mastering the fundamentals and real-world applications of Deep Learning. The curriculum explores key areas such as deep machine learning, deep structured learning, and hierarchical learning.
Deep Learning training is offered in two formats: "online live training" or "onsite live training." Online live training (also known as "remote live training") is delivered via an interactive, remote desktop. Onsite live training can be conducted locally at your customer premises in Prague or at NobleProg corporate training centers in Prague.
From Prague Main Train Station (Praha hlavní nádraží)
Take tram 9 from Hlavní nádraží toward Sídliště Řepy.
Get off at Újezd.
Walk about 10 minutes toward Malostranské náměstí / Prokopská.
Continue along Prokopská to 296/8.
Alternative: Take the metro C from Hlavní nádraží → Muzeum, change to metro A → Malostranská, then walk across Malá Strana. This involves more walking.
From Prague Bus Station — Florenc
Take metro B from Florenc toward Zličín.
Get off at Můstek.
Change to metro A toward Nemocnice Motol.
Get off at Malostranská.
Walk approximately 10–15 minutes to Prokopská 296/8.
This instructor-led, live training in Prague (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.
By the end of this training, participants will be able to:
Understand 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 Prague (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.
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 Prague (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 Prague (online or onsite) is targeted at advanced professionals aiming 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.
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.
This instructor-led, live training in Prague (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts looking to expand their knowledge and skills in deep learning for text-to-image generation.
By the end of this training, participants will be able to:
Understand advanced deep learning architectures and techniques for text-to-image generation.
Implement complex models and optimizations for high-quality image synthesis.
Optimize performance and scalability for large datasets and complex models.
Tune hyperparameters for better model performance and generalization.
Integrate Stable Diffusion with other deep learning frameworks and tools
This instructor-led, live training in Prague (online or onsite) targets advanced professionals seeking to leverage AI techniques to revolutionize drug discovery and development processes.
Upon completion of this training, participants will be able to:
Grasp the significance of AI in drug discovery and development.
Utilize machine learning methods to forecast molecular properties and interactions.
Deploy deep learning models for virtual screening and lead optimization.
Incorporate AI-driven methodologies into the clinical trial process.
This instructor-led, live training in Prague (online or onsite) is designed for biologists who want to understand how AlphaFold works and use AlphaFold models to guide 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 Prague (online or onsite) is tailored for beginner to intermediate-level developers seeking to utilize 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 designed for 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 its image generation workflow.
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 Prague, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.
By the end of this training, participants will be able to:
Implement machine learning algorithms and techniques for solving complex problems.
Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
Push Python algorithms to their maximum potential.
Use libraries and packages such as NumPy and Theano.
This hands-on training in Prague enables programmers to construct AI models from scratch using Python. You will gain mastery over supervised learning, neural networks, and unsupervised techniques through the use of scikit-learn and Apache Spark. The course emphasizes practical exercises in Jupyter notebooks to address real-world problems.
This instructor-led training in Prague covers the theoretical foundations and practical implementation of Deep Reinforcement Learning using Python. Participants will build and train DRL agents with TensorFlow or PyTorch, applying key algorithms like DQN and PPO to solve complex real-world problems.
A foundational course in Prague that delves into the basics of AI, ranging from intelligent agents to machine learning. It prepares executives and architects to evaluate emerging AI trends, implement practical solutions, and enhance business agility through automated strategies.
Explore how Machine Learning and Deep Learning transform the automotive sector. This Prague course covers core concepts from simple automation to autonomous decision-making, including neural networks and practical TensorFlow examples for real-world applications.
This three-day program in Prague delves into both the theoretical underpinnings and practical applications of Artificial Neural Networks, Machine Learning, and Deep Learning. Participants will gain insights into network architectures, learning dynamics, and mathematical principles, progressing from foundational perceptrons to sophisticated deep learning methodologies.
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 instructor-led live training in Prague (online or onsite) is tailored for software developers, data analysts, and technical professionals who wish to employ 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 Prague (online or on-site) targets data scientists who wish to utilize 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 live, instructor-led training, participants will master the use of Matlab to design, construct, and visualize convolutional neural networks (CNNs) for image recognition tasks.
Upon completion of this training, participants will be capable of:
Constructing deep learning models
Automating the data labeling process
Utilizing models developed in Caffe and TensorFlow-Keras
Training data across multiple GPUs, cloud environments, or clusters
Target Audience
Developers
Engineers
Domain experts
Course Format
A blend of lectures, discussions, exercises, and extensive hands-on practice
This instructor-led live training in Prague (available online or on-site) is designed for developers and data scientists who aim to utilize TensorFlow 2.x for building predictors, classifiers, generative models, and neural networks.
By the end of this training, participants will have the capability to:
Install and configure TensorFlow 2.x.
Understand the benefits of TensorFlow 2.x over previous iterations.
Build deep learning models.
Implement advanced image classifiers.
Deploy deep learning models to cloud, mobile, and IoT platforms.
This 35-hour course in Prague delves into the fundamentals of deep neural networks, covering CNNs, RNNs, and generative models such as GANs. Participants will gain hands-on proficiency with Theano and TensorFlow, acquiring the skills to build, train, and deploy production-grade deep learning models for real-world scenarios.
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Testimonials (5)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
Magdalena - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data.
Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zaklad Uslugowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
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