Deep Learning Training in Patras

This instructor-led live training in Patras (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 instructor-led live training in Patras (online or onsite) is designed for advanced professionals who wish to expand their expertise in computer vision and investigate TensorFlow's capabilities for developing advanced vision models using Google Colab.

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

• Construct and train Convolutional Neural Networks (CNNs) using TensorFlow.
• Utilize Google Colab for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques specifically for computer vision applications.
• Deploy computer vision models for practical, real-world solutions.
• Employ transfer learning to optimize the performance of CNN models.
• Visualize and interpret outcomes from image classification models.

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

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

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

This instructor-led, live training session in Patras (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 Patras (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 in Patras (online or onsite) is aimed at beginner to intermediate-level data scientists and machine learning engineers who wish to improve the performance of their deep learning models.

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

• Understand the principles of distributed deep learning.
• Install and configure DeepSpeed.
• Scale deep learning models on distributed hardware using DeepSpeed.
• Implement and experiment with DeepSpeed features for optimization and memory efficiency.

This instructor-led, live training session Patras (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 Patras, 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 course empowers programmers and data analysts with the essential techniques needed to construct machine learning solutions from the ground up using Python. It explores fundamental concepts of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network designs. Participants will examine proven strategies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks for practical AI development. The program enables professionals to build effective ML models, assess algorithm constraints, and execute applied projects that address real-world challenges.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions by interacting with their environments. This technology drives numerous modern AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to acquire strategies, refine policies, and execute autonomous decisions through trial-and-error processes guided by reward mechanisms.

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. Participants will learn to construct intelligent agents capable of making autonomous decisions within complex environments.

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

• Comprehend the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
• Implement core RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents leveraging TensorFlow or PyTorch.
• Deploy DRL solutions in practical applications, including gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and enhance training performance using contemporary tools.

Format of the Course

• Interactive lectures accompanied by guided discussions.
• Practical exercises and real-world implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (for instance, substituting TensorFlow with PyTorch), please contact us to arrange.

An exploration of artificial intelligence fundamentals demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making across 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 insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This course explores the application of AI—specifically Machine Learning and Deep Learning—within the automotive sector. It aims to identify technologies that can be (potentially) utilized across various automotive scenarios: from basic automation and image recognition to autonomous decision-making.

An Artificial Neural Network is a computational data model utilized in the creation of Artificial Intelligence (AI) systems that can perform "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a specialized subset of Machine Learning.

This instructor-led live training in Patras (online or onsite) is designed for researchers and developers who wish to use Chainer to build and train neural networks in Python, ensuring the code is easy to debug.

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

• Set up the necessary development environment to begin developing neural network models.
• Define and implement neural network models using comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This instructor-led, live training in Patras (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 Patras (online or onsite) targets researchers and developers who wish to install, set up, customize, and utilize the DeepMind Lab platform to develop general artificial intelligence and machine learning systems.

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

• Customize DeepMind Lab to build and run an environment that suits learning and training needs.
• Use DeepMind Lab's 3D simulation environment to train learning agents in a first-person viewpoint.
• Facilitate agent evaluation to develop intelligence in a 3D game-like world.

This instructor-led live training in Patras (online or onsite) targets business analysts, data scientists, and developers who wish to build and implement deep learning models to accelerate revenue growth and solve problems in the business world.

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

• Understand the core concepts of machine learning and deep learning.
• Get insights on the future of business and industry with ML and DL.
• Define business strategies and solutions with deep learning.
• Learn how to apply data science and deep learning in solving business problems.
• Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.

This instructor-led live training in Patras (online or onsite) targets data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

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

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in Patras (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.

This instructor-led, live training in Patras (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale them up to operate across multiple GPUs in parallel.

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

• Set up the necessary development environment to begin running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

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 Patras (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

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

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

During this instructor-led, live training, participants will explore advanced Machine Learning techniques in R by building a practical, real-world application step-by-step.

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

• Understand and implement unsupervised learning techniques
• Utilize clustering and classification methods to make predictions based on real-world data.
• Visualize data to quickly gain insights, make informed decisions, and refine analytical approaches.
• Enhance the performance of machine learning models through hyper-parameter tuning.
• Deploy models into production for integration into larger applications.
• Apply advanced machine learning techniques to address challenges involving social network data, big data, and other domains.

This instructor-led, live training in Patras (online or on-site) is designed for developers and data scientists who intend to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and similar applications.

By the end 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 conceptual foundation in neural networks and broadly covers machine learning algorithms, deep learning (algorithms and applications).

Part-1 (40%) of this training focuses heavily on fundamentals, helping you select the appropriate technology such as 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%) of the training is extensively based on TensorFlow, Google's open-source software library API for Deep Learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers aiming to utilize TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend TensorFlow’s structure and deployment mechanisms
• be capable of performing installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-level tasks such as training models, building graphs, and logging