The Deep Learning for Advanced Data Analysis course in Budapest is an in-depth training course designed to help professionals apply deep learning techniques to analyze and interpret complex datasets.
From:
16-02-2026
Course Overview
Deep learning, a subset of artificial intelligence, enables organizations to extract insights from large, complex, and unstructured datasets. By leveraging neural networks and advanced modeling techniques, businesses can achieve breakthroughs in predictive analytics, natural language processing, and image or speech recognition.
This course delivers a step-by-step framework for applying deep learning in advanced data analysis. Participants will learn to design and train neural networks, evaluate models, and apply them to real-world scenarios ranging from business forecasting to intelligent automation.
At EuroQuest International Training, the course emphasizes practical application, combining technical skills with strategic insights to ensure participants can apply deep learning to solve organizational challenges.
Key Benefits of Attending
Learn deep learning fundamentals and architectures
Apply neural networks to complex data analysis problems
Use AI for predictive modeling and advanced analytics
Gain hands-on experience with deep learning frameworks
Enhance decision-making with advanced AI-driven insights
Why Attend
This course equips participants to harness deep learning as a powerful tool for advanced analytics, enabling organizations to uncover hidden patterns, predict outcomes, and innovate faster.
Course Methodology
Instructor-led technical lectures on deep learning
Hands-on labs using frameworks such as TensorFlow and PyTorch
Real-world case studies and data projects
Group simulations of predictive modeling challenges
Interactive peer learning sessions
Course Objectives
By the end of this ten-day training course, participants will be able to:
Understand deep learning concepts and neural network structures
Prepare and preprocess data for deep learning applications
Build and train deep learning models for predictive analysis
Apply convolutional and recurrent neural networks to real-world data
Evaluate and optimize deep learning model performance
Use deep learning for text, image, and speech analytics
Manage computational resources for model training and deployment
Integrate deep learning into business analytics strategies
Ensure ethical and responsible AI use in data analysis
Communicate AI-driven insights to executives and stakeholders
Develop scalable deep learning workflows for organizations
Create a roadmap for long-term AI adoption and innovation
Target Audience
Data scientists and advanced analysts
AI and machine learning engineers
IT and innovation managers
Business intelligence professionals
Researchers and academics in data science fields
Target Competencies
Deep learning model design and training
Neural network application in analytics
Predictive modeling with AI techniques
Data preprocessing and transformation
Evaluation and optimization of models
Communication of AI insights
Strategic AI-driven innovation
Course Outline
Unit 1: Introduction to Deep Learning and Data Analysis
Deep learning vs traditional machine learning
Applications across industries
Evolution of neural networks
Case studies of advanced AI adoption
Unit 2: Data Preparation and Preprocessing
Structured vs unstructured data challenges
Cleaning, normalization, and feature engineering
Handling big data for deep learning
Tools for data preprocessing
Unit 3: Fundamentals of Neural Networks
Perceptrons and feedforward networks
Activation functions and architectures
Training principles: gradient descent and backpropagation
Practical lab: building a simple neural network
Unit 4: Deep Learning Frameworks and Tools
TensorFlow and PyTorch fundamentals
Model training workflows
Cloud-based platforms for deep learning
Lab: training models using open datasets
Unit 5: Convolutional Neural Networks (CNNs)
CNN architecture and principles
Applications in image and video analysis
Transfer learning techniques
Lab: image classification with CNNs
Unit 6: Recurrent Neural Networks (RNNs) and LSTMs
Sequence modeling fundamentals
Natural language processing applications
Time-series forecasting with RNNs
Lab: sentiment analysis with LSTMs
Unit 7: Advanced Deep Learning Architectures
Generative Adversarial Networks (GANs)
Autoencoders for anomaly detection
Transformer models for NLP
Emerging trends in deep learning
Unit 8: Model Evaluation and Optimization
Metrics for classification and regression
Overfitting and regularization techniques
Hyperparameter tuning strategies
Practical lab: optimizing model accuracy
Unit 9: Deep Learning in Business Applications
Forecasting demand and market trends
AI in customer experience and personalization
Fraud detection with anomaly analysis
Case studies of enterprise AI
Unit 10: Deployment and Scalability of Models
From research to production deployment
Cloud and edge deployment strategies
Managing computational costs
CI/CD pipelines for AI models
Unit 11: Ethics and Responsible AI in Deep Learning
Bias and fairness in models
Explainability and transparency challenges
Legal and regulatory considerations
Frameworks for ethical AI adoption
Unit 12: Capstone Deep Learning Project
Group-based data analysis challenge
Building and training advanced neural networks
Presenting business insights from deep learning models
Action plan for organizational implementation
Closing Call to Action
Join this ten-day training course to master deep learning for advanced data analysis, enabling your organization to unlock predictive power, enhance insights, and drive innovation.
The Deep Learning for Advanced Data Analysis Training Courses in Budapest provide professionals with the knowledge and technical skills needed to design, train, and apply deep learning models to complex analytical challenges. Designed for data scientists, machine learning engineers, researchers, and analytical decision-makers, these programs focus on how advanced neural network architectures can uncover subtle patterns, improve predictive accuracy, and enable intelligent automation across a wide range of business and scientific applications.
Participants gain a solid grounding in deep learning fundamentals, including artificial neural networks, convolutional neural networks (CNNs), recurrent and sequence models, and transformer-based architectures. The courses emphasize practical implementation techniques for solving real-world problems such as image recognition, natural language understanding, anomaly detection, forecasting, and unstructured data analysis. Through hands-on exercises using popular deep learning frameworks, attendees learn to preprocess data, select model architectures, tune hyperparameters, and evaluate model performance.
These deep learning and advanced analytics training programs in Budapest also highlight best practices in MLOps, model deployment, and scalability. Participants examine how to integrate deep learning solutions into operational workflows, optimize computational resources in cloud and on-premise environments, and ensure performance monitoring throughout the model lifecycle. The curriculum addresses considerations such as data governance, model interpretability, and ethical use of AI to ensure responsible and transparent analytical outcomes.
The training environment incorporates collaborative problem-solving, case-based discussions, and practical lab sessions to enhance both conceptual understanding and applied technical capability.
Attending these training courses in Budapest offers participants exposure to a dynamic and growing data science community, benefiting from expert instruction and interaction with peers from diverse sectors.
By completing this specialization, participants will be equipped to leverage deep learning tools effectively, solve high-dimensional analytical challenges, and drive innovation through intelligent data modeling. They will be prepared to apply deep learning techniques in ways that strengthen decision-making, enhance analytical precision, and support forward-looking organizational strategy.