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好 那 我 們 就 繼 續 講 吧
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64.358996
65.124574
0.952918
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0_7_8.mp3
我 們 就 繼 續 講 吧
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42.709712
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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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64.896732
65.227439
0.95926
0.958023
0_17_19.mp3
那 第 一 個 解 法
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54.050328
54.050328
0.96923
0.969036
0_19_24.mp3
叫 做 selective synaptic plasticity
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0_24_25.mp3
那 從 字 面 上
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59.93721
60.007777
0.893211
0.888811
0_25_28.mp3
你 可 能 一 下 子 沒 有 辦 法 get 到 說
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66.862401
66.876857
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0_28_30.mp3
這 個 方 法 到 底 想 要 做 什 麼
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0_31_34.mp3
這 個 synaptic 是 突 觸 的 意 思
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24.440055
24.400554
0.954754
0.954555
0_34_37.mp3
就 是 我 們 腦 神 經 中 這 個
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45.486554
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0_37_39.mp3
神 經 跟 神 經 之 間 的 連 結
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56.605261
56.538808
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0.874564
0_39_41.mp3
這 個 叫 做 突 觸
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55.357144
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0_41_43.mp3
plasticity 呢
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0.9731
0_43_45.mp3
是 可 塑 性 的 意 思
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68.77179
68.912595
0.924982
0.925244
0_45_46.mp3
所 以 簡 單 來 說
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69.048671
69.048671
0.89723
0.897114
0_46_48.mp3
這 個 方 法 想 要 做 的 事 情 就 是
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63.278517
63.37801
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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_53_55.mp3
某 一 些 神 經 元
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0_55_58.mp3
或 某 一 些 神 經 元 間 的 連 結
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具 有 可 塑 性
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0_59_61.mp3
selective 的 意 思 就 是 說
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44.817004
45.149761
0.913995
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0_61_64.mp3
只 有 部 分 的 連 結 是 有 可 塑 性 的
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0.929736
0.93036
0_64_66.mp3
有 一 些 連 結 必 須 被 固 化
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0_66_68.mp3
它 必 須 不 能 夠 再 移 動
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0_68_71.mp3
不 能 夠 再 改 變 它 的 數 值
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67.413197
67.664605
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0_72_74.mp3
那 像 這 樣 的 方 法 又 叫 做
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70.209279
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0.989345
0_74_77.mp3
regularization based 的 方 法
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64.548254
64.465664
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0.936798
0_77_79.mp3
那 這 個 面 向
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70.048573
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0_79_80.mp3
這 個 研 究 的 面 向
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68.963455
68.963455
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0_80_82.mp3
在 lifelong learning 的 領 域 裡 面
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0_82_85.mp3
我 覺 得 是 發 展 得 最 完 整 的
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59.35785
59.434532
0.950691
0.958623
0_85_87.mp3
所 以 等 一 下 我 們 會 花 比 較 多 的 時 間
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61.487657
61.760286
0.972327
0.972392
0_87_90.mp3
來 講 selective synaptic plasticity
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62.17736
62.050816
0.983873
0.984151
0_90_91.mp3
那 另 外 兩 個 面 向 呢
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61.298859
61.379463
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0_91_95.mp3
我 們 都 只 用 一 兩 頁 投 影 片 很 快 地 帶 過
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63.787947
63.897622
0.968874
0.973086
0_95_98.mp3
那 你 會 發 現 作 業 裡 面 主 要 的 問 題
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66.022865
66.234703
0.955512
0.957282
0_98_99.mp3
也 都 集 中 在
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65.36812
65.367974
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0.924863
0_99_102.mp3
跟 regularization based 有 關 的 方 法 上 面
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67.066899
67.123629
0.970335
0.969651
0_103_105.mp3
好 那 我 們 先 來 想 一 下
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66.115513
67.066809
0.932955
0.933564
0_105_108.mp3
為 什 麼 catastrophic forgetting 這 件 事 情
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0_108_109.mp3
會 發 生 呢
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0_110_113.mp3
我 們 假 設 有 任 務 一 跟 任 務 二
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0_113_114.mp3
這 兩 個 任 務
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0_114_115.mp3
而 這 兩 個 任 務 呢
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70.179257
70.485411
0.940115
0.939367
0_115_118.mp3
我 們 假 設 我 們 的 模 型 只 有 兩 個 參 數
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69.944118
70.492936
0.85442
0.863111
0_118_120.mp3
θ1 跟 θ2
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68.513168
68.691004
0.935738
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0_120_122.mp3
那 當 然 一 個 模 型 通 常 有 上
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64.520362
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0.96525
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0_122_124.mp3
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0_127_130.mp3
好 那 這 個 投 影 片 上 這 兩 張 圖
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0_130_135.mp3
代 表 的 是 任 務 一 跟 任 務 二 的 loss function
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0_135_137.mp3
也 就 是 在 任 務 一 上 面
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66.686126
67.12041
0.949219
0.950127
0_137_141.mp3
如 果 你 的 θ1 跟 θ2
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0_141_142.mp3
設 不 一 樣 的 值
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0_142_144.mp3
你 就 會 有 不 一 樣 的 loss
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0_144_148.mp3
那 我 們 用 顏 色 來 代 表 loss 的 大 小
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代 表 loss 越 小
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68.978209
70.364432
0.981585
0.981585
0_160_162.mp3
顏 色 越 偏 白 色
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67.835286
68.190875
0.927846
0.928287
0_162_164.mp3
代 表 loss 越 大
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0_164_167.mp3
好 所 以 左 右 兩 張 圖 分 別 就 是 任 務 一
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62.478447
62.527249
0.956383
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0_167_170.mp3
跟 任 務 二 的 loss function
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57.13515
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0_170_172.mp3
也 就 是 他 們 的 error surface
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70.488268
70.683425
0.961363
0.959577
0_173_176.mp3
好 那 我 們 現 在 先 讓 模 型 訓 練 任 務 一
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0_176_178.mp3
那 模 型 怎 麼 訓 練 任 務 一 呢
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你 要 有 一 個 隨 機 初 始 化 的 參 數
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0_180_183.mp3
我 們 這 邊 叫 它 θ0
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0_184_186.mp3
然 後 我 們 會 用 gradient descent 的 方 法
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66.101394
67.110805
0.960704
0.959073
0_189_192.mp3
那 你 就 按 照 gradient 的 方 向 呢
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69.358245
69.892488
0.885499
0.888243
0_192_194.mp3
去 update θ0 的 參 數
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70.490599
70.942498
0.958231
0.958231
0_194_196.mp3
得 到 θb
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67.087331
67.974169
0.975245
0.975245
0_196_198.mp3
好 那 假 設 update 夠 多 次 數
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69.821509
69.907111
0.949066
0.948852
0_198_200.mp3
你 覺 得 loss 降 得 夠 低 了
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69.096609
69.296737
0.925049
0.916743
0_200_203.mp3
那 你 就 等 於 是 把 任 務 一 學 完 了
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65.49296
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0_203_205.mp3
那 假 設 任 務 一 學 完 後
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70.085432
70.114682
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0_205_208.mp3
我 們 得 到 的 參 數 是 θb
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0_208_212.mp3
接 下 來 我 們 得 繼 續 解 任 務 二
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0_212_213.mp3
你 就 把 θb
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68.271545
68.322452
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0_213_215.mp3
同 樣 的 參 數 拷 貝 過 來
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63.371397
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0_215_219.mp3
拷 貝 到 任 務 二 的 這 個 error surface 上 面
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66.247136
66.696317
0.956778
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0_219_220.mp3
注 意 一 下
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69.946837
69.963645
0.901483
0.90174
0_220_223.mp3
雖 然 左 右 兩 邊 error surface 是 不 一 樣 的
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0_223_226.mp3
但 是 θb 我 們 這 邊 指 的 是 同 一 組 參 數
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0_226_230.mp3
θb 是 用 任 務 一 訓 練 出 來 的 參 數
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0_230_233.mp3
我 們 現 在 把 它 用 在 任 務 二 上
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0_233_236.mp3
我 們 現 在 把 θb 放 在 任 務 二 上
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0_236_238.mp3
繼 續 去 做 訓 練
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0_239_240.mp3
那 在 任 務 二 上
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43.040219
48.340913
0.97628
0.976854
0_240_244.mp3
我 們 有 另 外 一 個 不 一 樣 的 error surface
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67.172945
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0.993426
0_244_246.mp3
根 據 這 個 任 務 二 的 error surface
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0_246_248.mp3
去 再 update 參 數
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68.421009
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0_248_252.mp3
那 我 們 可 能 會 把 θb 往 右 上 角 移
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69.323065
69.506883
0.984286
0.984286
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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