Whether delivered online or on-site, these instructor-led live training courses offer a comprehensive look at building Neural Networks through interactive discussion and practical application. Participants will learn to leverage a range of primarily open-source toolkits and libraries, while also exploring how to harness the power of advanced hardware such as GPUs and apply optimization techniques for distributed computing and big data. The curriculum is built upon widely used programming languages like Python, Java, and R, alongside robust libraries including TensorFlow, Torch, Caffe, and Theano. We balance theoretical concepts with hands-on implementation, covering various architectures such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).
Neural Network training is offered in two formats: "online live training" and "onsite live training." Online sessions, also known as remote live training, are conducted via an interactive remote desktop. Onsite training can take place directly at your customer premises in Prague or within NobleProg’s 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.
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 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 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 (3)
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
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
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