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0_533_534.mp3 | 越 靠 近 越 好 | [
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0_536_539.mp3 | 就 代 表 說 我 們 希 望 θi 跟 θbi 非 常 靠 近 | [
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0_547_548.mp3 | 好 那 這 個 呢 | [
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0_548_550.mp3 | 就 是 我 們 要 optimize | [
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0_586_587.mp3 | 如 果 某 一 個 參 數 i | [
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0_587_589.mp3 | 第 i 個 參 數 它 的 bi 很 大 | [
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0_610_611.mp3 | 那 意 味 著 什 麼 | [
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0_611_612.mp3 | 那 意 味 著 就 是 | [
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0_612_616.mp3 | 我 們 沒 有 給 我 們 的 θi 任 何 限 制 | [
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0_623_625.mp3 | 那 這 個 時 候 就 是 一 般 的 training | [
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0_625_628.mp3 | 就 會 有 catastrophic forgetting 的 問 題 | [
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0_643_646.mp3 | 這 個 極 端 叫 做 intransigence | [
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0_653_656.mp3 | 所 以 intransigence 的 意 思 是 說 | [
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0_656_658.mp3 | 假 設 你 現 在 bi 非 常 地 | [
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0_667_668.mp3 | 那 你 的 模 型 | [
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0_668_670.mp3 | 可 能 在 舊 的 任 務 上 不 會 遺 忘 | [
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0_709_711.mp3 | 好 那 再 等 一 下 | [
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0_711_712.mp3 | 在 文 獻 上 | [
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0_734_737.mp3 | 對 它 來 說 它 要 讓 loss 越 小 越 好 嘛 | [
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0_737_738.mp3 | 那 怎 麼 讓 loss 最 小 | [
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0_738_739.mp3 | 直 接 bi 等 於 0 | [
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0_739_740.mp3 | loss 就 最 小 了 | [
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0_740_743.mp3 | 所 以 如 果 你 讓 bi 直 接 用 learn 的 | [
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