Chain-of-thought prompting - Explained!
Let's talk about how language models can reason with chain-of-though prompting A parameter efficient fine tuning technique that makes use of a low rank adapter to (1) reduce storage required per task by decreasing the number of trainable parameters added to the network per task (2) remove inference latency ensuring the stored parameters are applied to the existing network architecture instead of adding more RESOURCES [1 š] Paper with Chain-of-thought prompting: https://arxiv.org/pdf/2201.11903 [2 š] Paper that introduced GPT-3: https://arxiv.org/pdf/2005.14165 ABOUT ME ā Subscribe: https://www.youtube.com/c/CodeEmporiu... š Medium Blog: Ā Ā /Ā dataemporiumĀ Ā š» Github: https://github.com/ajhalthor š LinkedIn: Ā Ā /Ā ajay-halthor-477974bbĀ Ā PLAYLISTS FROM MY CHANNEL ā Deep Learning 101: Ā Ā Ā ā¢Ā DeepĀ LearningĀ 101Ā Ā ā Natural Language Processing 101: Ā Ā Ā ā¢Ā NaturalĀ LanguageĀ ProcessingĀ 101Ā Ā ā Reinforcement Learning 101: Ā Ā Ā ā¢Ā ReinforcementĀ LearningĀ 101Ā Ā Natural Language Processing 101: Ā Ā Ā ā¢Ā NaturalĀ LanguageĀ ProcessingĀ 101Ā Ā ā Transformers from Scratch: Ā Ā Ā ā¢Ā NaturalĀ LanguageĀ ProcessingĀ 101Ā Ā ā ChatGPT Playlist: Ā Ā Ā ā¢Ā ChatGPTĀ Ā MATH COURSES (7 day free trial) š Mathematics for Machine Learning: https://imp.i384100.net/MathML š Calculus: https://imp.i384100.net/Calculus š Statistics for Data Science: https://imp.i384100.net/AdvancedStati... š Bayesian Statistics: https://imp.i384100.net/BayesianStati... š Linear Algebra: https://imp.i384100.net/LinearAlgebra š Probability: https://imp.i384100.net/Probability OTHER RELATED COURSES (7 day free trial) š ā Deep Learning Specialization: https://imp.i384100.net/Deep-Learning š Python for Everybody: https://imp.i384100.net/python š MLOps Course: https://imp.i384100.net/MLOps š Natural Language Processing (NLP): https://imp.i384100.net/NLP š Machine Learning in Production: https://imp.i384100.net/MLProduction š Data Science Specialization: https://imp.i384100.net/DataScience š Tensorflow: https://imp.i384100.net/Tensorflow CHAPTERS 0:00 Introduction 1:02 Why CoT prompting? 2:05 Few Shot Learning 3:57 Reasoning 5:55 What is Chain of Thought? 6:09 Performance & benefits 7:07 Quiz 8:13 Conclusion
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