Training: PyTorch
Level
AdvancedDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedTraining: PyTorch
The PyTorch training is an intensive two-day course, with 80% focused on hands-on workshops and 20% on theory. The course is designed to provide participants with both solid theoretical foundations and practical skills in using PyTorch – one of the most popular machine learning frameworks. During the training, participants will work with real datasets, build and train models, and deploy them in production environments.
What will you learn?
- How to install and configure PyTorch in your working environment
- How to build, train, and optimize machine learning models with PyTorch
- How to implement advanced neural networks such as CNNs and RNNs
- How to prepare and deploy PyTorch models in production environments
Required technical skills
- Basic knowledge of Python programming
- Basic knowledge of machine learning
- Ability to work in Jupyter Notebook or Google Colab environments
Who is this training for?
Developers and data engineers who want to expand their skills with PyTorch-
Data scientists who want to apply PyTorch in their projects
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AI and ML enthusiasts eager to start working with PyTorch
Training Program
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Day 1: Fundamentals of ML Model Security
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Module 1: Introduction to ML ecosystem threats
- Characteristics of modern AI model attacks
- Consequences of successful breaches
- Case studies of intrusions and manipulations in real-world projects
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Module 2: Types of attacks on ML models
- Adversarial attacks: methods of generating adversarial samples
- Attacks on training data privacy
- Information leakage from trained models
- Vulnerability analysis of different ML architectures
- Attacks targeting ML infrastructure
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Module 3: Workshop – Threat identification
- Simulating attacks on sample classification and regression models
- Analyzing traces and penetration mechanisms of ML models
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Day 2: Advanced Protection Techniques
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Module 4: Methods for securing ML models
- Adversarial training techniques
- Federated learning for enhanced privacy
- Implementing obfuscation and data privacy mechanisms
- Strategies for risk reduction in ML workflows
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Module 5: Workshop – Practical model protection
- Designing resilient ML architectures
- Implementing advanced defense techniques
- Security testing of ML models
- Developing security policies for ML teams
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Module 6: Security tools and frameworks
- Overview of open-source tools for model protection
- Analysis of specialized ML cybersecurity libraries
- Automating security verification processes
- Integrating security tools with ML pipelines
