GANs and variadic autoencoders in Python
The training focuses on advanced generative artificial intelligence techniques using Generative Adversarial Networks (GANs) and variational autoencoders. Participants will learn the theoretical underpinnings and practical applications of these architectures in data generation and processing. The program combines theoretical lectures with intensive programming workshops, during which participants implement their own generative models.
Issues
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GAN architecture and operation
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Mathematical basis of variational autoencoders
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Loss functions in generative models
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Training stabilization techniques
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Variants of the GAN architecture
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Metrics for evaluating the quality of the generated data
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Style transfer and image manipulation
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Optimization of hyperparameters
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Problems and challenges in training generative models
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Applications of generative models
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Interpretation of the hidden space
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Latest trends in generative models
Benefits
- Implementing and training GANs and variational autoencoders in Python
- Will master techniques for designing and tuning architectures for generative machine learning models
- Will learn to effectively implement different variants of GANs and autoencoders to solve specific business problems
- Will gain the ability to evaluate the quality of generated data and optimize the learning process
- Will learn the latest trends and developments in the field of generative models
Who is this training for?
Prerequisites
- Advanced knowledge of programming in Python
- Basic knowledge of machine learning
- Knowledge of linear algebra and probability calculus
- Experience working with deep learning libraries
Training program
Generator and discriminator architecture
- Loss functions and the learning process
- Problems of training stability
- Regularization techniques
- Implementation of various GAN architectures
DCGAN for image generation
- Conditional GAN
- CycleGAN for style transfer
Progressive GAN
- Variational autoencoders (VAE)
- Theory and mathematical basis of VAE
- Implementation of a basic VAE
Modifications and improvements to the architecture
- Practical applications
- Advanced techniques and optimization
- GAN-VAE Hybrids
- Techniques for improving the quality of generated data
Model evaluation metrics
- Latest trends in generative models
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 GANs and variadic autoencoders in Python training for?
This training is designed for professionals looking to develop skills in gans and variadic autoencoders in python. Required level: advanced.
How long is the GANs and variadic autoencoders in Python training?
The training lasts 2. 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
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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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