Offered as either online or on-site, these instructor-led live Deep Learning (DL) training sessions use hands-on exercises to explore the core principles and practical uses of Deep Learning. The curriculum includes topics such as deep machine learning, deep structured learning, and hierarchical learning.
You can access Deep Learning training through "online live training" or "onsite live training". Online live training, also referred to as "remote live training", is conducted using an interactive remote desktop. Onsite live training can be delivered directly at your premises in Athens or at NobleProg's 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 tailored for intermediate-level developers, data scientists, and AI practitioners looking to leverage TensorFlow Lite for Edge AI applications.
By the conclusion 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 live, instructor-led training on Athens (delivered online or onsite) is tailored for advanced professionals looking to strengthen their grasp of computer vision and investigate TensorFlow’s potential for creating complex vision models using Google Colab.
Upon completion of this training, participants will be able to:
Develop 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 boost the performance of CNN models.
Visualize and interpret the results of image classification models.
This instructor-led, live training in Athens (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 Athens (online or onsite) is tailored for advanced-level professionals who wish 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.
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 instructor-led, live training in Athens (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts who aim 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 session in Athens (online or onsite) targets advanced professionals eager to leverage AI techniques to transform drug discovery and development processes.
Upon completion of this training, participants will be equipped to:
Grasp the significance of AI in the drug discovery and development lifecycle.
Utilize machine learning methods to forecast molecular properties and interactions.
Employ deep learning architectures for virtual screening and lead optimization.
Incorporate AI-driven methodologies into clinical trial procedures.
This instructor-led live training, offered in Athens (via online or onsite delivery), is designed for biologists who wish to understand the mechanisms of AlphaFold and apply its models to guide their experimental studies.
By the conclusion of this training, participants will be able to:
Understand the core principles of AlphaFold.
Learn how AlphaFold operates.
Learn how to interpret AlphaFold predictions and results.
This instructor-led, live training session Athens (online or onsite) targets beginner to intermediate-level developers seeking to utilize Large Language Models for various natural language tasks.
Upon completion of this training, participants will be equipped to:
Establish a development environment integrated with a popular LLM.
Construct a foundational LLM and fine-tune it using custom datasets.
Apply LLMs to diverse natural language tasks, such as summarizing text, answering questions, and generating content.
Debug and evaluate LLM performance utilizing tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
This instructor-led, live training in (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 instructor-led live training in Athens, participants will explore the most pertinent and state-of-the-art machine learning techniques in Python. Through the creation of a series of demonstrative applications involving image, music, text, and financial data, learners will gain practical expertise.
Upon completion of this training, participants will be able to:
Implement machine learning algorithms and techniques to address complex challenges.
Apply deep learning and semi-supervised learning methods to applications dealing with image, music, text, and financial datasets.
Maximize the potential of Python algorithms.
Utilize libraries and packages such as NumPy and Theano.
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 instructor-led live training in Athens (online or onsite) targets 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 Athens (online or onsite) targets data scientists who wish to utilize TensorFlow for analyzing potential fraud data.
By the conclusion of this training, participants will be able to:
Create a fraud detection model using Python and TensorFlow.
Build linear regressions and 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 utilize Matlab to design, construct, and visualize a convolutional neural network for image recognition.
Upon completing this training, participants will be capable of:
Constructing a deep learning model
Automating the data labeling process
Utilizing models from Caffe and TensorFlow-Keras
Training data across multiple GPUs, cloud environments, or clusters
Audience
Developers
Engineers
Domain experts
Format of the course
A combination of lectures, discussions, exercises, and extensive hands-on practice
This instructor-led live training in Athens, offered online or onsite, targets developers and data scientists looking to utilize TensorFlow 2.x for building predictors, classifiers, generative models, and neural networks.
By the end of the program, participants will be capable of:
Installing and configuring TensorFlow 2.x.
Understanding the benefits of TensorFlow 2.x over prior versions.
Building deep learning models.
Implementing an advanced image classifier.
Deploying deep learning models to cloud, mobile, and IoT devices.
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 (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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