We apply methods of machine-learning, such as neural networks, manifold learning and image processing, in order to study amoebae in algebraic geometry and string theory. With the help of embedding manifold projection, we recover complicated conditions obtained from so-called lopsidedness. For certain cases (e.g. lopsided amoeba with positive coefficients for $F_0$), it could even reach $\sim99\%$ accuracy. Using weights and biases, we also find good approximations to determine the genus for an amoeba at lower computational cost. In general, the models could easily predict the genus with over $90\%$ accuracies. With similar techniques, we also investigate the membership problem.

翻译：我们运用机器学习方法,如神经网络、多重学习和图像处理等,以便在代数几何学和弦理论中研究阿莫伊巴。通过嵌入多重投影,我们恢复了从所谓的偏斜性中获得的复杂条件。对于某些情况(如偏斜的阿莫伊巴,正系数为F$0),甚至可以达到美元。我们用权重和偏差,还发现良好的近似值,以较低的计算成本来确定阿莫伊巴的基因。一般来说,模型可以很容易地预测出超过90美圆的基因。我们用类似的技术来调查会员问题。