Tek görevli vs. çok görevli öğrenim

Büyük dil modelleri hem tek görevli hem de çok görevli öğrenimi kullanabilir. Mevcut LLM’lerin ve kullanım alanlarının yer aldığı bir listeyi inceliyorsun ve hangilerinin tek görevli, hangilerinin çok görevli öğrenim modeli olduğunu belirlemen gerekiyor.

Bunu öğrenmek, gelecekteki projelerin için doğru yaklaşımı seçmene yardımcı olacak.

Her iki tekniğin de kendine özgü avantajları ve olası kullanım senaryoları vardır ve her biri model performansı ile doğruluğunu önemli ölçüde etkileyebilir.

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Large Language Models (LLMs) Kavramları

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Large Language Models (LLMs) Kavramları

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Kursa Ücretsiz Başlayın

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: AI dünyasında LLM’lerin yükselişi Exercise 2: Bir LLM'in tanımı Exercise 3: Yapay zekâ dünyasında LLM'ler Exercise 4: AI ve LLM uygulamaları Exercise 5: Gerçek dünya uygulamaları Exercise 6: İş uygulamaları Exercise 7: Multimodal uygulamalar Exercise 8: Veri odaklı görevleri otomatikleştir Exercise 9: Dil modellemenin zorlukları Exercise 10: Bir dil modeli neler yapabilir?Exercise 11: Tek görevli vs. çok görevli öğrenim

Geçerli Egzersiz

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: Building blocks to train LLMs Exercise 2: Masked language Exercise 3: Predict the next word Exercise 4: Building from scratch Exercise 5: Introducing the transformer Exercise 6: Relationships between distant words Exercise 7: Transformer components Exercise 8: Attention mechanisms Exercise 9: Focus of multi-head attention Exercise 10: Self vs. multi-head attention Exercise 11: Advanced fine-tuning Exercise 12: End-to-end training Exercise 13: Training, tuning & feedback Exercise 14: Building an LLM

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