Self vs. multi-head attention

Bir AI geliştirme ekibinde veri analistisın. Mevcut projen, bir dil modelinde self-attention ve multi-head attention kavramlarını anlamayı ve uygulamayı içeriyor. Aşağıdaki konuşma veri kümesinden gelen ifadeleri düşün.

Etkileşimli uygulamayı kullanarak self-attention (metin içindeki belirli kelimeler arasındaki ilişkiler) ile multi-head attention (metnin birden çok yönünü aynı anda işleme) arasındaki farkı keşfet.

Hangi analiz görevleri self-attention’a, hangileri multi-head attention’a örnektir?

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Large Language Models (LLMs) Kavramları

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Uygulamalı etkileşimli egzersiz

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Large Language Models (LLMs) Kavramları

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Kursa Ücretsiz Başla

The AI landscape is evolving rapidly, and Large Language Models (LLMs) are at the forefront of this evolution. This chapter examines how LLMs are advancing the development of human-like artificial intelligence and transforming industries through their numerous applications. You will explore the challenges and complexity associated with language modeling.

Exercise 1: The rise of LLMs in the AI landscape Exercise 2: Definition of an LLM Exercise 3: LLMs in the AI landscape Exercise 4: AI vs. LLM applications Exercise 5: Real-world applications Exercise 6: Business applications Exercise 7: Multimodal applications Exercise 8: Automate data-driven tasks Exercise 9: Challenges of language modeling Exercise 10: What can a language model do?Exercise 11: Single vs. multi-task learning

This chapter emphasizes the novelty of LLMs and their emergent capabilities while outlining various NLP techniques for data preparation. You will learn the challenges of training LLMs and how fine-tuning can effectively address them. You will also understand how N-shot learning techniques enable efficient adaptation of pre-trained models when faced with limited labeled data.

Exercise 1: Novelty of LLMs Exercise 2: Problem solving with LLMs Exercise 3: Traditional models vs. LLMs Exercise 4: Generalized overview of NLP Exercise 5: Data preparation Exercise 6: Text preprocessing and representation Exercise 7: Word embeddings over bag-of-words Exercise 8: Fine-tuning Exercise 9: Challenges in building LLMs Exercise 10: Adapt a pre-trained model Exercise 11: Pre-trained or fine-tuned?Exercise 12: Learning techniques Exercise 13: Fine-tune a model Exercise 14: N-shot learning

In this chapter, you will learn about the fundamental building blocks of training an LLM, such as pre-training techniques. You'll also gain an intuitive understanding of complex concepts like transformer architecture, including the attention mechanism. The chapter discusses an advanced fine-tuning technique and summarizes the training process to complete an LLM.

Exercise 1: LLM’leri eğitmenin yapı taşları Exercise 2: Maskelenmiş dil Exercise 3: Sonraki kelimeyi tahmin et Exercise 4: Sıfırdan inşa etmek Exercise 5: Transformer’a giriş Exercise 6: Uzak kelimeler arasındaki ilişkiler Exercise 7: Transformer bileşenleri Exercise 8: Dikkat mekanizmaları Exercise 9: Çok başlı dikkatin odağı Exercise 10: Self vs. multi-head attention

Geçerli egzersiz

Exercise 11: Gelişmiş ince ayar Exercise 12: Uçtan uca eğitim Exercise 13: Eğitim, ayarlama ve geri bildirim Exercise 14: Bir LLM Oluşturma

In this chapter, we delve into the key considerations when training LLMs, such as large data availability, data quality, accurate labeling, and the implications of biased data. You will also examine various LLM risks like data privacy, ethical concerns, and environmental impact. Lastly, the chapter concludes by discussing emerging research areas and the evolving landscape of LLMs.

Exercise 1: Data concerns and considerations Exercise 2: Is your model fair?Exercise 3: Un-biased and relevant Exercise 4: Customer service of a bank Exercise 5: Ethical and environmental concerns Exercise 6: Responsible use Exercise 7: Ethics and environment Exercise 8: Where are LLMs heading?Exercise 9: Creativity vs. efficiency Exercise 10: Analyzing literary works Exercise 11: Time to wrap-up