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DecoderOnly Transformers ChatGPTs specific Transformer Clearly Explained
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=bQ5BoolX9Ag
Transformers are taking over AI right now, and quite possibly their most famous use is in ChatGPT. ChatGPT uses a specific type of Transformer called a Decoder-Only Transformer, and this StatQuest shows you how they work, one step at a time. And at the end (at 32:14), we talk about the differences between a Normal Transformer and a Decoder-Only Transformer. BAM! • NOTE: If you're interested in learning more about Backpropagation, check out these 'Quests: • The Chain Rule: • The Chain Rule • Gradient Descent: • Gradient Descent, Step-by-Step • Backpropagation Main Ideas: • Neural Networks Pt. 2: Backpropagatio... • Backpropagation Details Part 1: • Backpropagation Details Pt. 1: Optimi... • Backpropagation Details Part 2: • Backpropagation Details Pt. 2: Going ... • If you're interested in learning more about the SoftMax function, check out: • • Neural Networks Part 5: ArgMax and So... • If you're interested in learning more about Word Embedding, check out: • Word Embedding and Word2Vec, Clearly ... • If you'd like to learn more about calculating similarities in the context of neural networks and the Dot Product, check out: • Cosine Similarity: • Cosine Similarity, Clearly Explained!!! • Attention: • Attention for Neural Networks, Clearl... • If you'd like to learn more about Normal Transformers, see: • Transformer Neural Networks, ChatGPT'... • If you'd like to support StatQuest, please consider... • Patreon: / statquest • ...or... • YouTube Membership: / @statquest • ...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... • https://statquest.org/statquest-store/ • ...or just donating to StatQuest! • paypal: https://www.paypal.me/statquest • venmo: @JoshStarmer • Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: • / joshuastarmer • 0:00 Awesome song and introduction • 1:34 Word Embedding • 7:26 Position Encoding • 10:10 Masked Self-Attention, an Autoregressive method • 22:35 Residual Connections • 23:00 Generating the next word in the prompt • 26:23 Review of encoding and generating the prompt • 27:20 Generating the output, Part 1 • 28:46 Masked Self-Attention while generating the output • 30:40 Generating the output, Part 2 • 32:14 Normal Transformers vs Decoder-Only Transformers • #StatQuest
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