Whether you prefer online or onsite environments, our instructor-led live Deep Learning (DL) training courses offer hands-on practical experience to help you master the core principles and real-world applications of the field. The curriculum covers key areas such as deep machine learning, deep structured learning, and hierarchical learning.
We offer Deep Learning training in two formats: "online live training" or "onsite live training". Online live training, also known as "remote live training," is conducted through an interactive remote desktop. Onsite live training can be delivered locally at your premises in Plovdiv or at NobleProg’s corporate training centers in Plovdiv.
NobleProg -- Your Local Training Provider
Business Center Plovdiv
Han Kubrat St 1, Plovdiv, Bulgaria, 4017
This is the most modern business center in the city, with all the necessary functionalities, while being located in a green part of the city.
It is about 20 minutes by bus from the main train station as well as the city center.
This instructor-led, live training in Plovdiv (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 session in Plovdiv (delivered online or onsite) is designed for advanced professionals looking to refine their understanding of computer vision. It explores the full potential of TensorFlow for creating sophisticated vision models using Google Colab.
By the conclusion of this training, participants will be equipped to:
Develop and train convolutional neural networks (CNNs) with TensorFlow.
Utilize Google Colab to achieve scalable and efficient cloud-based model development.
Apply image preprocessing methods tailored for computer vision workflows.
Deploy computer vision models into real-world operational settings.
Optimize CNN model performance by leveraging transfer learning.
Visualize and analyze the results generated by image classification models.
This instructor-led, live training in Plovdiv (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 Plovdiv (online or on-site) is designed for advanced 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 Plovdiv (online or onsite) is designed for advanced professionals who wish to explore state-of-the-art XAI techniques for deep learning models, focusing on the development of interpretable AI systems.
Upon completion of this training, participants will be able to:
Grasp the challenges associated with explainability in deep learning.
Apply advanced XAI techniques to neural networks.
Interpret the decisions generated by deep learning models.
Assess the balance between model performance and transparency.
This instructor-led, live training in Plovdiv (available online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision professionals seeking to expand their knowledge and skills in deep learning for text-to-image generation.
By the conclusion of this training, participants will be able to:
Understand advanced deep learning architectures and techniques specific to text-to-image generation.
Implement sophisticated models and optimizations aimed at high-quality image synthesis.
Optimize performance and scalability for processing large datasets and complex models.
Tune hyperparameters to achieve superior model performance and generalization.
Integrate Stable Diffusion with other deep learning frameworks and tools.
This instructor-led, live training in Plovdiv (online or onsite) targets advanced-level professionals seeking to leverage AI techniques to revolutionize drug discovery and development processes.
By the end of this training, participants will be able to:
Understand the role of AI in drug discovery and development.
Apply machine learning techniques to predict molecular properties and interactions.
Use deep learning models for virtual screening and lead optimization.
Integrate AI-driven approaches into the clinical trial process.
This instructor-led, live training in Plovdiv (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.
By the end of this training, participants will be able to:
Grasp the fundamental principles of AlphaFold.
Learn how AlphaFold operates.
Master the interpretation of AlphaFold predictions and results.
This instructor-led, live training in Plovdiv (online or onsite) targets beginner to intermediate developers looking 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, available 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 functions 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 session in Plovdiv, participants will learn the most relevant and cutting-edge machine learning techniques in Python by building 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 Plovdiv enables developers to construct AI models using Python from first principles. You will gain proficiency in supervised learning, neural networks, and unsupervised methods by leveraging scikit-learn and Apache Spark. The course emphasizes practical Jupyter exercises designed for real-world problem-solving.
This instructor-led training in Plovdiv delves into the theoretical basis and practical application of Deep Reinforcement Learning with Python. Participants will construct and train DRL agents using TensorFlow or PyTorch, leveraging key algorithms like DQN and PPO to address complex real-world challenges.
A foundational training module in Plovdiv that covers AI fundamentals, from intelligent agents to machine learning. It prepares executives and architects to evaluate emerging AI trends, incorporate practical solutions, and enhance business agility through automated strategies.
Discover how Machine Learning and Deep Learning are reshaping the automotive landscape. This Plovdiv course explores fundamental concepts ranging from simple automation to autonomous decision-making, featuring neural networks and practical TensorFlow examples tailored for real-world applications.
This three-day intensive on Plovdiv integrates the theoretical underpinnings with practical applications of Artificial Neural Networks, Machine Learning, and Deep Learning. Attendees will examine various network architectures, learning algorithms, and the associated mathematical foundations, progressing from foundational perceptrons to sophisticated deep learning methodologies.
This instructor-led, live training in Plovdiv (online or onsite) offers 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.
Upon completion of this training, participants will be able to:
Apply fundamental statistical methods to pattern recognition.
Utilize essential models such as neural networks and kernel methods for data analysis.
Implement advanced techniques to solve complex problems.
Enhance prediction accuracy by integrating various models.
This instructor-led live training in Plovdiv (offered online or onsite) is intended for software developers, data analysts, and technical experts who aim 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 Plovdiv (online or on-site) targets data scientists aiming to utilize TensorFlow for analyzing potential fraud data.
By the conclusion of this training, participants will be able to:
Build a fraud detection model using Python and TensorFlow.
Implement linear regressions and models to predict fraud.
Develop a complete AI application for fraud data analysis.
In this instructor-led, live training, participants will learn how to utilize Matlab to design, construct, and visualize a convolutional neural network for the purpose of image recognition.
Upon completion of 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 utilizing multiple GPUs, cloud environments, or clusters
Audience
Developers
Engineers
Domain experts
Format of the course
Combination of lectures, discussions, exercises, and extensive hands-on practice
This instructor-led, live training course in Plovdiv, delivered either online or on-site, is designed for developers and data scientists aiming to utilize TensorFlow 2.x for building predictors, classifiers, generative models, neural networks, and other complex systems.
By the end of this training, participants will be equipped to:
Install and configure the TensorFlow 2.x environment.
Understand the key benefits of TensorFlow 2.x over previous versions.
This 35-hour course in Plovdiv delves into the fundamentals of deep neural networks, covering CNNs, RNNs, and generative models such as GANs. Participants will gain practical experience 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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