Training: Large Language Models
Level
AdvancedDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedTraining: Large Language Models
The Large Language Models training focuses on both the scientific and practical exploration of large language models (LLMs). These powerful AI systems are capable of processing and generating human-like text. Models such as GPT (Generative Pre-trained Transformer) are characterized by a massive number of parameters and are trained on enormous datasets, enabling them to capture complex language patterns and produce coherent texts in different styles and contexts. LLMs are widely applied in areas such as content generation, machine translation, chatbots, sentiment analysis, and more. Their growing popularity makes them an increasingly essential tool in natural language processing (NLP) and artificial intelligence.
What will you learn?
- Preparing text data: Learn how to prepare text data for LLMs, including data cleaning, tokenization, and formatting
- Practical implementation of LLMs: Gain hands-on experience in applying LLMs in various use cases, such as text generation and chatbot development
- Understanding LLM fundamentals: Acquire knowledge of LLM architecture, functionality, and training processes
- Ethics and security awareness: Understand ethical challenges associated with LLMs and learn techniques for securing models against potential risks
Software developers and engineers who want to expand their knowledge of language models and learn how to apply them effectively in practice-
AI and machine learning specialists who want to deepen their expertise in language models
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Data analysts and data scientists who want to use LLMs for analyzing and processing large text datasets
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Professionals working with sentiment analysis, natural language processing, and other text-related tasks
Training Program
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Introduction
- Fundamentals of text processing and preparing text data
- Theoretical overview of Large Language Models (concepts, architecture, training process)
- Introduction to LLMs
- Applying concepts with basic code examples
- Evaluation of model outputs
- Case study
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Hands-on Work with LLMs
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Practical applications
- Implementing and exploring real-world LLM use cases such as text generation and chatbots
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Fine-tuning
- Adapting pre-trained models to specific use cases and project needs
- Tools and frameworks supporting LLMs
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Ethics & Security
- Ethical considerations in working with large language models
- Techniques for mitigating risks and ensuring safe use of LLMs
