(Principal Component Analysis) Suppose that all data are in a space, we first find the direction of high- est variance of these data points, then the direction of second-highest variance that is orthogonal to

engs = ['go .', "i lost .", 'he\'s calm .', 'i\'m home .'] fras = ['va !', 'j\'ai perdu .', 'il est calme .', 'je suis chez moi .'] for eng, fra in zip(engs, fras): translation, attention_weight_seq fra, k=2):.3f}') go . => va !, bleu 1.000 i lost . => j'ai perdu ., bleu 1.000 he's calm . => il est riche ., bleu 0.658 i'm home . => je suis en retard ?, bleu 0.447 小结 • 根据“编码器‐解码器”架构的设计，我们可以使用两 engs = ['go .', "i lost .", 'he\'s calm .', 'i\'m home .'] fras = ['va !', 'j\'ai perdu .', 'il est calme .', 'je suis chez moi .'] for eng, fra in zip(engs, fras): translation, dec_attention_weight_seq