ai-agent-book 精选快照(<2MB 代码与文档,来自 github.com/bojieli/ai-agent-book)
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<!DOCTYPE html>
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<html>
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<head><style>
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html { background: #14102E; }
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body { width: 720pt; height: 405pt; margin: 0; padding: 0; font-family: Arial, sans-serif; display: flex; background-image: url('../assets/cover-bg.png'); }
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.left { width: 440pt; margin: 44pt 0 44pt 48pt; display: flex; flex-direction: column; }
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.kicker { font-size: 10pt; color: #F59E0B; font-weight: bold; letter-spacing: 2pt; margin: 0 0 14pt 0; }
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h1 { font-size: 40pt; color: #FFFFFF; margin: 0 0 14pt 0; line-height: 1.1; }
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.sub { font-size: 14pt; color: #DDD6FE; margin: 0 0 26pt 0; line-height: 1.35; }
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.authors { font-size: 11pt; color: #EDE9FE; margin: 0 0 6pt 0; line-height: 1.4; }
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.affil { font-size: 9.5pt; color: #B8AEE8; margin: 0; }
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</style></head>
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<body>
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<div class="left">
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<p class="kicker">NEURIPS 2017 · ARXIV:1706.03762</p>
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<h1>Attention Is<br>All You Need</h1>
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<p class="sub">The Transformer: sequence transduction based entirely on attention — no recurrence, no convolution.</p>
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<div class="rule"></div>
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<p class="authors">Ashish Vaswani · Noam Shazeer · Niki Parmar · Jakob Uszkoreit<br>Llion Jones · Aidan N. Gomez · Łukasz Kaiser · Illia Polosukhin</p>
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<p class="affil">Google Brain · Google Research · University of Toronto</p>
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</div>
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<div class="card">
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<img src="../source_visuals/fig1-03.png">
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<p class="cardcap">The Transformer — model architecture<br>Figure 1, paper p. 3</p>
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</div>
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</body>
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</html>
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