Technologies / Artificial Intelligence

Python in natural language generation

The training guides participants through advanced natural language generation techniques using Python, combining the theoretical foundations of NLG with practical implementation of modern generative models. The program is structured so that participants learn the entire spectrum of text generation methods - from simple rule-based techniques to advanced models based on transformer architectures. The classes are conducted in the form of workshops, where each theoretical concept is immediately translated into practical implementations using real use cases. Participants work with popular libraries such as Transformers, GPT and T5, learning not only implementation, but also ethical aspects and best practices in text generation.

Issues

Generative text models
Transformer architectures
Sampling techniques
Generation control
Conversational systems
Generating abstracts
Machine translation
Optimization of models
Generation evaluation
Ethics in the NLG
Deployment of models
Performance monitoring
Configuration and implementation
Managing security policies
Configuration of authentication and authorization
Implementation of access control
Integration with cloud services
Monitoring and analysis
Reporting and analytics tools
Threat detection
Network traffic analysis
Responding to incidents
Advanced platform features
Performance optimization
Integration with SIEM solutions
Security automation
Best implementation practices

Benefits

Upon completion of the training, the participant will be able to design and implement advanced natural language generation systems
He or she will gain the ability to customize generative models to meet specific application needs
He will be able to create high-quality conversational systems and text generators
Will learn to effectively use state-of-the-art language models in generative tasks
Will master techniques for controlling and optimizing the text generation process
Will gain practical experience in implementing generative models in a production environment

Who is this training for?

ML engineers working on generative models

AI developers developing chatbots and assistants

Data scientists specializing in NLP

Conversational app developers

Specialists in content creation automation

Researchers involved in text generation

Architects of AI solutions for language processing

Prerequisites

Advanced knowledge of Python
Experience in deep learning and NLP
Knowledge of the basics of language modeling
Understanding the architecture of transformers

Training program

Sampling and decoding techniques
Control of generated content
Methods of text quality evaluation

Transformer models in the generation of
Fine-tuning language models

Techniques for increasing diversity

Building conversational systems
Generating summaries and paraphrases

Generation of descriptions and reports
Optimization and implementation

Techniques for accelerating inference
Monitoring the quality of generation
Error handling and edge cases

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 Python in natural language generation we recommend: Advanced knowledge of Python; Experience in deep learning and NLP; Knowledge of the basics of language modeling.

What is the format and duration of this training?

The training lasts 3 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: ML engineers working on generative models; AI developers developing chatbots and assistants; Data scientists specializing in NLP.