Neural Networks
Large Language Models rely on neural networks—complex structures inspired by the human brain—to identify and learn patterns in text data for generating coherent responses.
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Syntytyka VDR
Click the button to start the virtual dance room experience. Watch the grid and background pulse to the beat!
Multimodal Language Learning Scenarios
Engage in interactive scenarios that not only simulate real-life conversations but also provide multimodal outputs—text, image, and voice—to enhance your language learning experience.
Hello! I need help practicing my Spanish today.
¡Hola! ¿Qué te gustaría practicar: vocabulario, gramática, o conversación?
Let's start with vocabulary related to food.
Perfecto. Nombra tres frutas en español.
Manzana, naranja, y plátano.
Text
"Manzana, naranja, y plátano."
Image
Voice
Audio sample: "¡Hola, cómo estás?"
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µLLM Intent Builder
Python
React
AI/ML
Break down vague ideas into concrete steps using AI
Simple Input/Output
Streamlined process from concept to implementation
AI-Powered
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Multiple Options
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Refinement Loop
Iterate and perfect your solution
µLLM Demo
Explore LLM Concepts
Learn key ideas behind Large Language Models and their capabilities.
Transformer Architecture
Transformers revolutionized NLP by processing words in parallel, understanding long-range context, and enabling effective pre-training on massive datasets.
Natural Language Processing (NLP)
NLP techniques help machines interpret human language—detecting syntax, semantics, and sentiment—to deliver more accurate text understanding.
Emergent Abilities
When scaled sufficiently, LLMs can display unexpected talents—like reasoning or creative writing— beyond their original training scope.
Prompt Engineering
Crafting questions or instructions (“prompts”) in a strategic manner helps guide LLMs towards more relevant, accurate, and context-aware outputs.
Fine-Tuning
Fine-tuning adapts a general LLM to a specific domain or task by continuing training on smaller, specialized datasets—boosting domain-specific performance.
Tokenization
Language is broken into “tokens” (words, subwords, or symbols) before processing. Proper tokenization is crucial for handling text efficiently in LLM pipelines.
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