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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_24_25.mp3 | 那 從 字 面 上 | [
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0_25_28.mp3 | 你 可 能 一 下 子 沒 有 辦 法 get 到 說 | [
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0_39_41.mp3 | 這 個 叫 做 突 觸 | [
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0_43_45.mp3 | 是 可 塑 性 的 意 思 | [
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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_74_77.mp3 | regularization based 的 方 法 | [
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0_77_79.mp3 | 那 這 個 面 向 | [
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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_108_109.mp3 | 會 發 生 呢 | [
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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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|
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|
0_170_172.mp3 | 也 就 是 他 們 的 error surface | [
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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_203_205.mp3 | 那 假 設 任 務 一 學 完 後 | [
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0_205_208.mp3 | 我 們 得 到 的 參 數 是 θb | [
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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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