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Introduction to large language models

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L15: Pre-training & fine tuning | gpt architecture adaptation & text generation

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L14: Causal language modelling | decoder only transformers & autoregressive text generation

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L13: Transformers for language modelling | encoder only decoder only & encoder decoder architectures

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L11: Language modelling | pre training foundation for large language models

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L12: Introduction to language modelling - motivation | fine tuning in GPT & transformers

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L10: Layer normalization | normalization in transformers encoder decoder architecture explained

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L8: Batch normalization | residual connections and layer normalization in transformers

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L5: Sinusoidal encoding & sequence order

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L6: Zooming into decoder layer | decoding transformers masked self attention &cross attention

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L7: Positional encoding motivation methods & limitations

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L9: Teacher forcing & masked attention | autoregressive decoding with masking in transformers

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L4: Multi-headed attention in transformers explained

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L3: Self-attention in transformers encoder & contextual word embeddings

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L2: Attention is all you need transformer architecture explained

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L1: Introduction to transformer architecture

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