Training: Advanced Image Analysis with CNNs
Level
IntermediateDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedTraining: General AI Training – AI from Scratch
The Advanced Image Analysis with CNNs training is an intensive workshop designed to introduce participants to the latest deep learning techniques for image analysis. The course focuses on the practical use of Convolutional Neural Networks (CNNs), enabling you to build high-performance models for image recognition, object segmentation, and classification of complex visual data. The program is based on up-to-date tools and the TensorFlow library, combining theory with hands-on exercises — perfect for applications in AI, medicine, industry, and research.
What will you learn?
- How to design and train advanced CNNs using TensorFlow and Keras
- Techniques to prevent overfitting and methods to improve model performance in image tasks
- How to interpret CNNs using visualization and analysis of filters and activation maps
- The ability to independently execute complex image analysis projects using cutting-edge AI architectures and tools
Who is this training for?
Individuals who want to expand their skills in image analysis using CNNs and already have basic knowledge of machine learning and Python-
Professionals looking to explore advanced techniques for building and optimizing CNN architectures
-
Participants aiming to work on real-world projects in Computer Vision and Deep Learning
-
AI enthusiasts interested in exploring the latest trends and tools used in industry and scientific research
What will you learn?
- Understand the basics of AI and LLMs – explained simply, without technical jargon
- Configure and effectively use AI tools – from ChatGPT and Copilots to graphic and video applications
- Create effective prompts – to get precise and useful AI outputs
- Use AI in multimedia creation – graphics, presentations, audio, and video
- Build and implement AI agents – to handle documents, knowledge bases, and internal processes
- Consciously adopt AI in your organization – understanding opportunities, risks, and legal/ethical implications
- Develop future-ready skills that will be crucial on the job market
Training Program
-
Day 1: Introduction and CNN Fundamentals
-
Module 1: Basics of Convolutional Neural Networks
- Operation of convolutional, pooling, and fully connected layers
- Representation of digital images as input tensors; processing RGB and grayscale images
- Tools and libraries for efficient image dataset management
-
Module 2: Building and Training a Basic CNN Model
- Practical exercises implementing simple CNN architectures in TensorFlow/Keras
- Strategies for preventing overfitting: dropout, batch normalization, L2 regularization
- Data preprocessing, image augmentation, and basic model optimization techniques
-
Module 1: Basics of Convolutional Neural Networks
-
Day 2: Advanced Techniques and Model Optimization
-
Module 3: Advanced Architectures and Regularization
- Analysis of more complex CNN architectures (e.g., ResNet, Inception) and their applications
- Advanced regularization strategies and augmentation methods
-
Module 4: Improving Model Quality and Interpretability
- Automated hyperparameter optimization with KerasTuner and Optuna
- Optimization methods: learning rate tuning, transfer learning strategies, early stopping
- Tools and techniques for CNN interpretability, including visualization of filters and activation maps
-
Module 3: Advanced Architectures and Regularization
-
Day 3: Practical Projects and Applications
-
Module 5: Applying CNNs to Real-World Problems
- Image segmentation and object detection in medical, industrial, and other domains
- Introduction to combining CNNs with other techniques (RNNs, GANs) for advanced image analysis tasks
-
Module 6: Project Workshop and Summary
- Team-based work on selected image analysis problems
- Presentation of results, discussion of best practices, and exploration of trends in computer vision
-
Module 5: Applying CNNs to Real-World Problems
Contact us
we will organize training for you tailored to your needs
Error: Contact form not found.