loss : function的失误率,采用loss最低的那个function
supervise learning
reinforce learning
Network Architecture:定义函数的搜寻范围,包括RNN,CNN
RNN -> Seq2seq
CNN -> GAN
Regression->Classification->RNN/CNN
CNN -> Unsupervised Learning(Auto-encoder) -> Anomaly Detection -> Transfer Learning (Domain Adversarial Learning) -> Meta Learning -> Life-long Learning -> Reinforecement Learning
CNN -> Explainable AI -> Adversarial Attack -> Network Compression
Explainable AI: 解释为何function可变辨识
Adversarial Attack:应对杂乱信息与攻击
Domain Adversarial Learning: 训练资料和测试资料分布很像,如果不接近呢
Meta Learning: Learn to Learn,让机器学习如何学习
Life-long Learning: 终身学习,又叫 Continuous Learning, Nerver End Learning
graph TB A((Regression)); B((Classification)); C>RNN]; D>CNN]; E[\Seq2seq\]; F[\GAN\]; G>Unsupervised Learning]; H>Anomaly Detection]; I>Transfer Learning]; J(Meta Learning); K[\Life-long Learning\]; L[\Reinforecement Learning\]; M(Explainable AI); N>Adversarial Attack]; O{Network Compression}; A-->B; B-->C; C-->E; B-->D; D-->G; D-->F; D-->M; M-->N; N-->O; G-->H; H-->I; I-->J; J-->K; K-->L;