Deep learning in computer vision with Caffe
The training focuses on the practical use of the Caffe framework in computer vision tasks using deep learning. The program covers the implementation of various convolutional network architectures, training techniques and model optimization. Participants will learn the specifics of working with Caffe and best practices in computer vision application development.The training introduces participants to the world of machine learning automation through the use of AutoML tools. The program covers both the theoretical basics of automation and the practical application of leading AutoML solutions. During the workshop, participants will learn methods for automatic model selection, hyperparameter optimization and feature engineering. The classes are conducted in an interactive format, where theory is immediately verified in practical tasks.
Issues
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Framework Caffe
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Convolutional networks
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Transfer learning
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Data augmentation
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Optimization of models
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CNN debugging
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Performance profiling
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Deployment of models
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Integration of systems
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Performance monitoring
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Scaling up solutions
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Best practices
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----------|------------- Subcategory | Artificial Intelligence Training code | IT-AI-157 Duration | 2 days (16 hours) Price net/person | 2450 PLN.
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AutoML systems architecture
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Automatic feature engineering
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Optimization of hyperparameters
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Model selection
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Cross-validation
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Ensemble learning
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Performance monitoring
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Pipeline ML
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Version management
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Integration of systems
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Update models
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Optimization strategies
Benefits
- Practical knowledge in the implementation of computer vision models using the Caffe framework
- He or she will learn to design and train efficient convolutional networks for a variety of image analysis tasks
- Will learn techniques for optimizing and tuning models in the Caffe environment
- Will be able to implement computer vision solutions in a production environment
- Will develop the ability to diagnose and troubleshoot CNN models
- Will gain the ability to integrate Caffe models into existing systems.The participant will gain the ability to effectively use AutoML tools in daily work with machine learning models
- Will learn to automate time-consuming data preparation and model selection processes
- Will learn ML pipeline optimization techniques for performance and quality of results
- Will be able to implement AutoML solutions in a production environment
- Will gain knowledge of best practices for monitoring and updating automated models
- Will develop the ability to evaluate and compare different AutoML platforms
Who is this training for?
Prerequisites
- Knowledge of the basics of convolutional networks
- Programming experience in C++ or Python
- Basic knowledge of computer vision
- Knowledge of deep learning issues
- Basic knowledge of machine learning
- Experience in working with ML models
- Knowledge of model validation processes
- Python programming basics
Training program
Architecture of the framework
- Configuring the environment
- Prototxt and modeling
- Data management
- Implementation of models
CNN Architecture
- Transfer learning
- Fine-tuning of models
- Data augmentation
- Optimization and efficiency
Training strategies
- Debugging models
- Performance optimization
- Profiling
- Production implementation
Deployment of models
- Integration with applications
- Performance monitoring
Scaling up solutions
- Introduction to AutoML
- AutoML solution concepts and architecture
- Overview of available platforms and tools
- ML pipeline automation
- Optimization strategies
- Automatic feature engineering
- Feature selection and transformation
- Dimensionality reduction
- Encoding of categorical variables
- Handling missing data
- Optimization of models
- Model selection algorithms
- Techniques for tuning hyperparameters
Cross-validation
- Ensemble learning
- Implementation and monitoring
- Integration with existing systems
- Performance monitoring
- Update models
- Version management
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
Who is the Deep learning in computer vision with Caffe training for?
This training is designed for professionals looking to develop skills in deep learning in computer vision with caffe. Required level: intermediate.
How long is the Deep learning in computer vision with Caffe training?
The training lasts 3. Available in online or on-site format.
Will I receive a certificate?
Yes — every participant receives a completion certificate confirming acquired competencies. EITT holds ISO 9001 accreditation.
Can this training be conducted for a closed group?
Yes — we offer dedicated closed trainings for companies. We customize the program to your team's needs. Contact us for an individual quote.
Request a quote
Funding Options
Check funding options for your company
Development Services Database
Up to 80% funding for SMEs from EU funds
Check availabilityNational Training Fund
Up to 100% funding for employers
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