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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_39_41.mp3 | 這 個 叫 做 突 觸 | [
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0_48_53.mp3 | 我 們 只 讓 我 們 的 這 個 類 神 經 網 路 中 | [
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0_77_79.mp3 | 那 這 個 面 向 | [
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0_80_82.mp3 | 在 lifelong learning 的 領 域 裡 面 | [
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0_87_90.mp3 | 來 講 selective synaptic plasticity | [
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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_173_176.mp3 | 好 那 我 們 現 在 先 讓 模 型 訓 練 任 務 一 | [
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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_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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