Technologies / Artificial Intelligence

Deep Learning in NLP - advanced techniques

Advanced training in deep learning techniques in natural language processing. The program focuses on the practical application of the latest neural network architectures in NLP tasks. Participants learn advanced models and implementation techniques through hands-on workshops, working on real use cases. The classes combine theory with intensive programming and experiments.

Issues

Neural network architectures in NLP
Attention mechanisms and transformers
BERT-based language models
Generative language models
Transfer learning in NLP
Regularization and standardization techniques
Optimization of deep learning models
Multilingual neural models
Processing of long sequences
Interpretation of language models
Implementation of models in production
The latest trends in deep learning for NLP

Benefits

The participant will master advanced techniques for implementing deep learning models in NLP tasks
Will gain practical skills in designing and training custom neural architectures
Will develop a deep understanding of how modern language models and transformers work
Will learn to optimize models for performance and quality of results
Will learn the latest trends and developments in deep learning for NLP
Will be able to independently implement and tune advanced language models

Who is this training for?

Machine learning engineers with NLP experience

AI researchers specializing in language processing

Python programmers working with deep learning models

AI solution architects

Data science specialists in language projects

Developers of advanced NLP systems

Artificial intelligence experts

Prerequisites

Advanced knowledge of Python and deep learning libraries
Solid knowledge of machine learning
Experience in NLP projects
Knowledge of the basics of neural networks

Training program

Attention mechanisms and transformers
Encoding-decoding architectures

Advanced language models
BERT architecture and operation
Fine-tuning of transformer models

Zero-shot and few-shot learning

GPT architecture and its variants

Sampling and decoding techniques
Optimization of generated sequences
Practical applications and optimization
Implementation of custom layers and modules
Regularization and standardization techniques
Optimize performance and memory consumption
Implementing models in a production environment

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

What are the prerequisites for this training?

For Deep Learning in NLP - advanced techniques we recommend: Advanced knowledge of Python and deep learning libraries; Solid knowledge of machine learning; Experience in NLP projects.

What is the format and duration of this training?

The training lasts 4 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Machine learning engineers with NLP experience; AI researchers specializing in language processing; Python programmers working with deep learning models.