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0_10_12.mp3 | 好 那 我 們 接 下 來 呢 | [
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0_12_17.mp3 | 就 是 要 講 三 個 lifelong learning 的 可 能 解 法 | [
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0_31_34.mp3 | 這 個 synaptic 是 突 觸 的 意 思 | [
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0_41_43.mp3 | plasticity 呢 | [
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0_45_46.mp3 | 所 以 簡 單 來 說 | [
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0_77_79.mp3 | 那 這 個 面 向 | [
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0_79_80.mp3 | 這 個 研 究 的 面 向 | [
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0_90_91.mp3 | 那 另 外 兩 個 面 向 呢 | [
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0_99_102.mp3 | 跟 regularization based 有 關 的 方 法 上 面 | [
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0_103_105.mp3 | 好 那 我 們 先 來 想 一 下 | [
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0_114_115.mp3 | 而 這 兩 個 任 務 呢 | [
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0_115_118.mp3 | 我 們 假 設 我 們 的 模 型 只 有 兩 個 參 數 | [
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0_135_137.mp3 | 也 就 是 在 任 務 一 上 面 | [
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0_137_141.mp3 | 如 果 你 的 θ1 跟 θ2 | [
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0_142_144.mp3 | 你 就 會 有 不 一 樣 的 loss | [
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0_173_176.mp3 | 好 那 我 們 現 在 先 讓 模 型 訓 練 任 務 一 | [
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0_189_192.mp3 | 那 你 就 按 照 gradient 的 方 向 呢 | [
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||
0_192_194.mp3 | 去 update θ0 的 參 數 | [
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0_194_196.mp3 | 得 到 θb | [
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||
0_196_198.mp3 | 好 那 假 設 update 夠 多 次 數 | [
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0_198_200.mp3 | 你 覺 得 loss 降 得 夠 低 了 | [
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0_200_203.mp3 | 那 你 就 等 於 是 把 任 務 一 學 完 了 | [
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0_239_240.mp3 | 那 在 任 務 二 上 | [
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0_253_255.mp3 | 那 得 到 θ* | [
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0_255_258.mp3 | θ* 是 訓 練 完 任 務 一 | [
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