Offered as either online or onsite instructor-led sessions, our live Neural Network training programs guide you through the process of building Neural Networks using a wide range of predominantly open-source toolkits and libraries. Participants will also learn to leverage advanced hardware, such as GPUs, and employ optimization techniques that incorporate distributed computing and big data. Our courses are built around widely adopted programming languages like Python, Java, and R, alongside robust libraries including TensorFlow, Torch, Caffe, and Theano. These programs cover both theoretical foundations and practical implementation, featuring various neural network architectures such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).
Neural Network training is available in two formats: “online live training” or “onsite live training.” Online live training, also known as “remote live training,” is delivered through an interactive remote desktop. Onsite live training can be conducted at customer locations in Athens or at NobleProg corporate training centers in Athens.
Take Metro Line 3 (M3) from Airport toward Dimotiko Theatro.
Get off at Evangelismos.
The address is on Vasilissis Sofias Avenue, very close to the station. Evangelismos station is directly on the avenue.
Allow roughly 40–50 minutes total, depending on waiting/walking time.
From Athens Central Railway Station (Larissa Station)
Take the metro to Syntagma and change to Line 3 toward Doukissis Plakentias/Airport.
Get off at Evangelismos.
Walk to No. 46.
By bus
There are several buses running along Vasilissis Sofias Avenue. Nearby stops include Rigilis Street and Syntagma, while the Evangelismos area is served by multiple bus routes.
The metro is generally the simplest option, especially if you're arriving with luggage.
This instructor-led, live training in Athens (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building 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 practical training in Athens enables programmers to develop AI models from scratch using Python. You will gain expertise in supervised learning, neural networks, and unsupervised techniques through the use of scikit-learn and Apache Spark. The course emphasizes hands-on Jupyter exercises designed to solve real-world problems.
This instructor-led training on Athens delves into the theoretical basis and practical application of Deep Reinforcement Learning using Python. Learners will construct and train DRL agents with TensorFlow or PyTorch, leveraging algorithms such as DQN and PPO to address complex real-world challenges.
A foundational training module in Athens that covers the basics of AI, ranging from intelligent agents to machine learning. It empowers executives and architects to evaluate new AI trends, integrate actionable solutions, and boost business agility through automated strategies.
Discover how Machine Learning and Deep Learning are reshaping the automotive sector. This Athens course delves into fundamental concepts, progressing from simple automation to fully autonomous decision-making. It also covers neural networks and demonstrates practical TensorFlow implementations for real-world applications.
This three-day program in Athens delves into both the theoretical underpinnings and practical applications of Artificial Neural Networks, Machine Learning, and Deep Learning. Attendees will examine various network architectures, learning mechanisms, and the mathematical principles involved, progressing from foundational perceptrons to sophisticated deep learning methodologies.
This instructor-led, live training in Athens (online or onsite) provides 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 35-hour course in Athens explores the fundamentals of deep neural networks, including CNNs, RNNs, and generative models such as GANs. Attendees gain practical experience with Theano and TensorFlow, learning to construct, 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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