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Opening Credits
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2 preface
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3 Introduction to language model development
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4 Basics of natural language processing
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5 Choosing the right framework
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6 collecting and preprocessing data
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7 model architecture design
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8 training and fine tuning
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9 evaluation metrics and validation
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10 deploying your language model
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11 fine tuning for specific use cases
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12 handling ethical and bias considerations
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13 optimizing performance and efficiency
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14 popular large language models
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15 popular large language models gpt 3 (generative pre trained transformer 3)
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16 popular large language models bert (bidirectional encoder representations from transformers)
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17 popular large language models t5 (text to text transfer transformer)
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18 popular large language models xlnet
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19 popular large language models roberta (robustly optimized bert approach)
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20 popular large language models llama 2
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21 popular large language models google's gemini
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22 integrating language model with applications
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23 scaling and distributed training
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24 continuous improvement and maintenance
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25 interpretable ai and explainability
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26 challenges and future trends
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27 case studies and project examples
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28 community and collaboration
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29 conclusion
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30 glossary
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31 bibliography
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Ending Credits
1/7/2024